1
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Jeon YR, Seo D, Lee Y, Akinwande D, Choi C. The 3D Monolithically Integrated Hardware Based Neural System with Enhanced Memory Window of the Volatile and Non-Volatile Devices. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2402667. [PMID: 38884186 DOI: 10.1002/advs.202402667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/08/2024] [Indexed: 06/18/2024]
Abstract
3D neuromorphic hardware system is first demonstrated in neuromorphic application as on-chip level by integrating array devices with CMOS circuits after wafer bonding (WB) and interconnection process. The memory window of synaptic device is degraded after WB and 3 Dimesional (3D) integration due to process defects and thermal stress. To address this degradation, Ag diffusion in materials of Ta2O5 and HfO2 is studied in a volatile memristor, furthermore, the interconnection and gate metal Ru are investigated to reduce defective traps of gate interface in non-volatile memory devices. As a result, a memory window is improved over 106 in both types of devices. Improved and 3D integrated 12 × 14 array devices are identified in the synaptic characteristics according to the change of the synaptic weight from the interconnected Test Element Group (TEG) of the Complementary Metal Oxide Semiconductor (CMOS) circuits. The trained array devices present recognizable image of letters, achieving an accuracy rate of 92% when utilizing a convolutional neural network, comparing the normalized accuracy of 93% achieved by an ideal synapse device. This study proposes to modulate the memory windows up to 106 in an integrated hardware-based neural system, considering the possibility of device degradation in both volatile and non-volatile memory devices demonstrated by the hardware neural system.
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Affiliation(s)
- Yu-Rim Jeon
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, 78172, USA
| | - Donguk Seo
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Yoonmyung Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Deji Akinwande
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, 78172, USA
| | - Changhwan Choi
- Division of Materials Science and Engineering, Hanyang University, Seoul, 04763, South Korea
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2
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An H, Li Y, Ren Y, Wan Y, Wang W, Sun Z, Zhong J, Peng Z. High-performance flexible resistive random-access memory based on SnS 2 quantum dots with a charge trapping/de-trapping effect. NANOSCALE 2024. [PMID: 38832816 DOI: 10.1039/d4nr00745j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
The application of resistive random-access memory (RRAM) in storage and neuromorphic computing has attracted widespread attention. Benefitting from the quantum effect, transition metal dichalcogenides (TMD) quantum dots (QDs) exhibit distinctive optical and electronic properties, which make them promising candidates for emerging RRAM. Here, we show a high-performance forming-free flexible RRAM based on high-quality tin disulfide (SnS2) QDs prepared by a facile liquid phase method. The RRAM device demonstrates high flexibility with a large on/off ratio of ∼106 and a long retention time of over 3 × 104 s. The excellent switching behavior of the memristor is elucidated by a charge trapping/de-trapping mechanism where the SnS2 QDs act as charge trapping centers. This study is of significance for the understanding and development of TMD QD-based flexible memristors.
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Affiliation(s)
- Hua An
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), School of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.
| | - Yiyang Li
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), School of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.
| | - Yi Ren
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), School of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.
| | - Yili Wan
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), School of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.
| | - Weigao Wang
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), School of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.
| | - Zhenhua Sun
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), School of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.
| | - Junwen Zhong
- Department of Electromechanical Engineering, University of Macau, Macau, SAR, 999078, China.
| | - Zhengchun Peng
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), School of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
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3
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Wang J, Ren Y, Yang Z, Lv Q, Zhang Y, Zhang M, Zhao T, Gu D, Liu F, Tang B, Yang W, Lin Z. Synergistically Modulating Conductive Filaments in Ion-Based Memristors for Enhanced Analog In-Memory Computing. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2309538. [PMID: 38491732 PMCID: PMC11165545 DOI: 10.1002/advs.202309538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/05/2024] [Indexed: 03/18/2024]
Abstract
Memristors offer a promising solution to address the performance and energy challenges faced by conventional von Neumann computer systems. Yet, stochastic ion migration in conductive filament often leads to an undesired performance tradeoff between memory window, retention, and endurance. Herein, a robust memristor based on oxygen-rich SnO2 nanoflowers switching medium, enabled by seed-mediated wet chemistry, to overcome the ion migration issue for enhanced analog in-memory computing is reported. Notably, the interplay between the oxygen vacancy (Vo) and Ag ions (Ag+) in the Ag/SnO2/p++-Si memristor can efficiently modulate the formation and abruption of conductive filaments, thereby resulting in a high on/off ratio (>106), long memory retention (10-year extrapolation), and low switching variability (SV = 6.85%). Multiple synaptic functions, such as paired-pulse facilitation, long-term potentiation/depression, and spike-time dependent plasticity, are demonstrated. Finally, facilitated by the symmetric analog weight updating and multiple conductance states, a high image recognition accuracy of ≥ 91.39% is achieved, substantiating its feasibility for analog in-memory computing. This study highlights the significance of synergistically modulating conductive filaments in optimizing performance trade-offs, balancing memory window, retention, and endurance, which demonstrates techniques for regulating ion migration, rendering them a promising approach for enabling cutting-edge neuromorphic applications.
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Affiliation(s)
- Jinyong Wang
- School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731P. R. China
- Department of Electrical and Computer EngineeringNational University of SingaporeSingapore117576Singapore
| | - Yujing Ren
- Department of Chemical and Biomolecular EngineeringNational University of SingaporeSingapore117585Singapore
| | - Ze Yang
- Department of Microelectronics and Integrated CircuitSchool of Electronic Science and EngineeringXiamen UniversityXiamen361005P. R. China
| | - Qiaoya Lv
- Department of Electrical and Computer EngineeringNational University of SingaporeSingapore117576Singapore
| | - Yu Zhang
- Department of Electronic Science and TechnologyHarbin Institute of TechnologyHarbin150001P. R. China
| | - Mingyue Zhang
- Department of Chemical and Biomolecular EngineeringNational University of SingaporeSingapore117585Singapore
| | - Tiancheng Zhao
- School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731P. R. China
| | - Deen Gu
- School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731P. R. China
| | - Fucai Liu
- School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731P. R. China
| | - Baoshan Tang
- Department of Electrical and Computer EngineeringNational University of SingaporeSingapore117576Singapore
| | - Weifeng Yang
- Department of Microelectronics and Integrated CircuitSchool of Electronic Science and EngineeringXiamen UniversityXiamen361005P. R. China
| | - Zhiqun Lin
- Department of Chemical and Biomolecular EngineeringNational University of SingaporeSingapore117585Singapore
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4
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Prudnikov NV, Emelyanov AV, Serenko MV, Dereven'kov IA, Maiorova LA, Erokhin VV. Modulation of polyaniline memristive device switching voltage by nucleotide-free analogue of vitamin B 12. NANOTECHNOLOGY 2024; 35:335204. [PMID: 38759638 DOI: 10.1088/1361-6528/ad4cf5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/17/2024] [Indexed: 05/19/2024]
Abstract
Memristive devices offer essential properties to become a part of the next-generation computing systems based on neuromorphic principles. Organic memristive devices exhibit a unique set of properties which makes them an indispensable choice for specific applications, such as interfacing with biological systems. While the switching rate of organic devices can be easily adjusted over a wide range through various methods, controlling the switching potential is often more challenging, as this parameter is intricately tied to the materials used. Given the limited options in the selection conductive polymers and the complexity of polymer chemical engineering, the most straightforward and accessible approach to modulate switching potentials is by introducing specific molecules into the electrolyte solution. In our study, we show polyaniline (PANI)-based device switching potential control by adding nucleotide-free analogue of vitamin B12, aquacyanocobinamide, to the electrolyte solution. The employed concentrations of this molecule, ranging from 0.2 to 2 mM, enabled organic memristive devices to achieve switching potential decrease for up to 100 mV, thus providing a way to control device properties. This effect is attributed to strong aromatic interactions between PANI phenyl groups and corrin macrocycle of the aquacyanocobinamide molecule, which was supported by ultraviolet-visible spectra analysis.
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Affiliation(s)
| | - Andrey V Emelyanov
- National Research Centre 'Kurchatov Institute', 123182 Moscow, Russia
- Moscow Institute of Physics and Technology (National Research University), 141701 Dolgoprudny, Moscow Region, Russia
| | - Maria V Serenko
- National Research Centre 'Kurchatov Institute', 123182 Moscow, Russia
- Moscow Institute of Physics and Technology (National Research University), 141701 Dolgoprudny, Moscow Region, Russia
| | - Ilia A Dereven'kov
- Institute of Macroheterocyclic Compounds, Ivanovo State University of Chemistry and Technology, 153000 Ivanovo, Russia
| | - Larissa A Maiorova
- Institute of Macroheterocyclic Compounds, Ivanovo State University of Chemistry and Technology, 153000 Ivanovo, Russia
- Federal Research Center Computer Science and Control of Russian Academy of Sciences, 119333 Moscow, Russia
| | - Victor V Erokhin
- Consiglio Nazionale delle Ricerche, Institute of Materials for Electronics and Magnetism (CNR-IMEM), 43124 Parma, Italy
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5
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Liu L, Wang D, Wang D, Sun Y, Lin H, Gong X, Zhang Y, Tang R, Mai Z, Hou Z, Yang Y, Li P, Wang L, Luo Q, Li L, Xing G, Liu M. Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware. Nat Commun 2024; 15:4534. [PMID: 38806482 PMCID: PMC11133408 DOI: 10.1038/s41467-024-48631-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 05/06/2024] [Indexed: 05/30/2024] Open
Abstract
We report a breakthrough in the hardware implementation of energy-efficient all-spin synapse and neuron devices for highly scalable integrated neuromorphic circuits. Our work demonstrates the successful execution of all-spin synapse and activation function generator using domain wall-magnetic tunnel junctions. By harnessing the synergistic effects of spin-orbit torque and interfacial Dzyaloshinskii-Moriya interaction in selectively etched spin-orbit coupling layers, we achieve a programmable multi-state synaptic device with high reliability. Our first-principles calculations confirm that the reduced atomic distance between 5d and 3d atoms enhances Dzyaloshinskii-Moriya interaction, leading to stable domain wall pinning. Our experimental results, supported by visualizing energy landscapes and theoretical simulations, validate the proposed mechanism. Furthermore, we demonstrate a spin-neuron with a sigmoidal activation function, enabling high operation frequency up to 20 MHz and low energy consumption of 508 fJ/operation. A neuron circuit design with a compact sigmoidal cell area and low power consumption is also presented, along with corroborated experimental implementation. Our findings highlight the great potential of domain wall-magnetic tunnel junctions in the development of all-spin neuromorphic computing hardware, offering exciting possibilities for energy-efficient and scalable neural network architectures.
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Affiliation(s)
- Long Liu
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Di Wang
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Dandan Wang
- Hubei Jiufengshan Laboratory, Wuhan, Hubei, 430206, China.
| | - Yan Sun
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Huai Lin
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiliang Gong
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Yifan Zhang
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ruifeng Tang
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhihong Mai
- Hubei Jiufengshan Laboratory, Wuhan, Hubei, 430206, China
| | - Zhipeng Hou
- Institute for Advanced Materials, South China Normal University, Guangzhou, 510006, China
| | - Yumeng Yang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Peng Li
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Lan Wang
- Lab of Low Dimensional Magnetism and Spintronic Devices, School of Physics, Hefei University of Technology, Hefei, 230009, Anhui, China
| | - Qing Luo
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ling Li
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guozhong Xing
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Ming Liu
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
- Frontier Institute of Chip and System, State Key Laboratory of Integrated Chips and Systems, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, China.
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6
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Guo Z, Zhang J, Wang J, Liu X, Guo P, Sun T, Li L, Gao H, Xiong L, Huang J. Organic Synaptic Transistors with Environmentally Friendly Core/Shell Quantum Dots for Wavelength-Selective Memory and Neuromorphic Functions. NANO LETTERS 2024; 24:6139-6147. [PMID: 38722705 DOI: 10.1021/acs.nanolett.4c01606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
Organic transistors based on organic semiconductors together with quantum dots (QDs) are attracting more and more interest because both materials have excellent optoelectronic properties and solution processability. Electronics based on nontoxic QDs are highly desired considering the potential health risks but are limited by elevated surface defects, inadequate stability, and diminished luminescent efficiency. Herein, organic synaptic transistors based on environmentally friendly ZnSe/ZnS core/shell QDs with passivating surface defects are developed, exhibiting optically programmable and electrically erasable characteristics. The synaptic transistors feature linear multibit storage capability and wavelength-selective memory function with a retention time above 6000 s. Various neuromorphic applications, including memory enhancement, optical communication, and memory consolidation behaviors, are simulated. Utilizing an established neuromorphic model, accuracies of 92% and 91% are achieved in pattern recognition and complicated electrocardiogram signal processing, respectively. This research highlights the potential of environmentally friendly QDs in neuromorphic applications and health monitoring.
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Affiliation(s)
- Ziyi Guo
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China
| | - Junyao Zhang
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China
| | - Jun Wang
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China
| | - Xu Liu
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China
| | - Pu Guo
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China
| | - Tongrui Sun
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China
| | - Li Li
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China
| | - Huaiyu Gao
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China
| | - Lize Xiong
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai 200434, P. R. China
| | - Jia Huang
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai 200434, P. R. China
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7
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Pashin DS, Bastrakova MV, Rybin DA, Soloviev II, Klenov NV, Schegolev AE. Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:854. [PMID: 38786810 PMCID: PMC11124324 DOI: 10.3390/nano14100854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/01/2024] [Accepted: 05/11/2024] [Indexed: 05/25/2024]
Abstract
In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of a system that implements XOR and OR logical operations.
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Affiliation(s)
- Dmitrii S. Pashin
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
| | - Marina V. Bastrakova
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
- Russian Quantum Centre, 143025 Moscow, Russia
| | - Dmitrii A. Rybin
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
| | - Igor. I. Soloviev
- Russian Quantum Centre, 143025 Moscow, Russia
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia;
- National University of Science and Technology MISIS, 119049 Moscow, Russia;
| | - Nikolay V. Klenov
- National University of Science and Technology MISIS, 119049 Moscow, Russia;
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Andrey E. Schegolev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia;
- Science Department, Moscow Technical University of Communication and Informatics (MTUCI), 111024 Moscow, Russia
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8
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Li X, Wan C, Zhang R, Zhao M, Xiong S, Kong D, Luo X, He B, Liu S, Xia J, Yu G, Han X. Restricted Boltzmann Machines Implemented by Spin-Orbit Torque Magnetic Tunnel Junctions. NANO LETTERS 2024; 24:5420-5428. [PMID: 38666707 DOI: 10.1021/acs.nanolett.3c04820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2024]
Abstract
Artificial intelligence has surged forward with the advent of generative models, which rely heavily on stochastic computing architectures enhanced by true random number generators with adjustable sampling probabilities. In this study, we develop spin-orbit torque magnetic tunnel junctions (SOT-MTJs), investigating their sigmoid-style switching probability as a function of the driving voltage. This feature proves to be ideally suited for stochastic computing algorithms such as the restricted Boltzmann machines (RBM) prevalent in pretraining processes. We exploit SOT-MTJs as both stochastic samplers and network nodes for RBMs, enabling the implementation of RBM-based neural networks to achieve recognition tasks for both handwritten and spoken digits. Moreover, we further harness the weights derived from the preceding image and speech training processes to facilitate cross-modal learning from speech to image generation. Our results clearly demonstrate that these SOT-MTJs are promising candidates for the development of hardware accelerators tailored for Boltzmann neural networks and other stochastic computing architectures.
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Affiliation(s)
- Xiaohan Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Caihua Wan
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Ran Zhang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Mingkun Zhao
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Shilong Xiong
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Dehao Kong
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Xuming Luo
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Bin He
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Shiqiang Liu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Jihao Xia
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Guoqiang Yu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Xiufeng Han
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
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9
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Stasner P, Kopperberg N, Schnieders K, Hennen T, Wiefels S, Menzel S, Waser R, Wouters DJ. Reliability effects of lateral filament confinement by nano-scaling the oxide in memristive devices. NANOSCALE HORIZONS 2024; 9:764-774. [PMID: 38511616 DOI: 10.1039/d3nh00520h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
Write-variability and resistance instability are major reliability concerns impeding implementation of oxide-based memristive devices in neuromorphic systems. The root cause of the reliability issues is the stochastic nature of conductive filament formation and dissolution, whose impact is particularly critical in the high resistive state (HRS). Optimizing the filament stability requires mitigating diffusive processes within the oxide, but these are unaffected by conventional electrode scaling. Here we propose a device design that laterally confines the switching oxide volume and thus the filament to 10 nm, which yields reliability improvements in our measurements and simulations. We demonstrate a 50% decrease in HRS write-variability for an oxide nano-fin device in our full factorial analysis of modulated current-voltage sweeps. Furthermore, we use ionic noise measurements to quantify the HRS filament stability against diffusive processes. The laterally confined filaments exhibit a change in the signal-to-noise ratio distribution with a shift to higher values. Our complementing kinetic Monte Carlo simulation of oxygen vacancy (re-)distribution for confined filaments shows improved noise behavior and elucidates the underlying physical mechanisms. While lateral oxide volume scaling down to filament sizes is challenging, our efforts motivate further examination and awareness of filament confinement effects in regards to reliability.
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Affiliation(s)
- Pascal Stasner
- Institut für Werkstoffe der Elektrotechnik II (IWE2) and JARA-FIT, RWTH Aachen University, Aachen 52074, Germany.
| | - Nils Kopperberg
- Institut für Werkstoffe der Elektrotechnik II (IWE2) and JARA-FIT, RWTH Aachen University, Aachen 52074, Germany.
| | - Kristoffer Schnieders
- Peter-Grünberg-Institut 7 (PGI-7), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Tyler Hennen
- Institut für Werkstoffe der Elektrotechnik II (IWE2) and JARA-FIT, RWTH Aachen University, Aachen 52074, Germany.
| | - Stefan Wiefels
- Peter-Grünberg-Institut 7 (PGI-7), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Stephan Menzel
- Peter-Grünberg-Institut 7 (PGI-7), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Rainer Waser
- Institut für Werkstoffe der Elektrotechnik II (IWE2) and JARA-FIT, RWTH Aachen University, Aachen 52074, Germany.
- Peter-Grünberg-Institut 7 (PGI-7), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
- Peter-Grünberg-Institut 10 (PGI-10), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Dirk J Wouters
- Institut für Werkstoffe der Elektrotechnik II (IWE2) and JARA-FIT, RWTH Aachen University, Aachen 52074, Germany.
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10
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Koh EK, Dananjaya PA, Poh HY, Liu L, Lee CXX, Thong JR, You YS, Lew WS. Unraveling the origins of the coexisting localized-interfacial mechanism in oxide-based memristors in CMOS-integrated synaptic device implementations. NANOSCALE HORIZONS 2024; 9:828-842. [PMID: 38450438 DOI: 10.1039/d3nh00554b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The forefront of neuromorphic research strives to develop devices with specific properties, i.e., linear and symmetrical conductance changes under external stimuli. This is paramount for neural network accuracy when emulating a biological synapse. A parallel exploration of resistive memory as a replacement for conventional computing memory ensues. In search of a holistic solution, the proposed memristive device in this work is uniquely poised to address this elusive gap as a unified memory solution. Opposite biasing operations are leveraged to achieve stable abrupt and gradual switching characteristics within a single device, addressing the demands for lower latency and energy consumption for binary switching applications, and graduality for neuromorphic computing applications. We evaluated the underlying principles of both switching modes, attributing the anomalous gradual switching to the modulation of oxygen-deficient layers formed between the active electrode and oxide switching layer. The memristive cell (1R) was integrated with 40 nm transistor technology (1T) to form a 1T-1R memory cell, demonstrating a switching speed of 50 ns with a pulse amplitude of ±2.5 V in its forward-biased mode. Applying pulse trains of 20 ns to 490 ns in the reverse-biased mode exhibited synaptic weight properties, obtaining a nonlinearity (NL) factor of <0.5 for both potentiation and depression. The devices in both modes also demonstrated an endurance of >106 cycles, and their conductance states were also stable under temperature stress at 85 °C for 104 s. With the duality of the two switching modes, our device can be used for both memory and synaptic weight-storing applications.
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Affiliation(s)
- Eng Kang Koh
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Putu Andhita Dananjaya
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Han Yin Poh
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Lingli Liu
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Calvin Xiu Xian Lee
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Jia Rui Thong
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Young Seon You
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Wen Siang Lew
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
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11
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Park J, Kumar A, Zhou Y, Oh S, Kim JH, Shi Y, Jain S, Hota G, Qiu E, Nagle AL, Schuller IK, Schuman CD, Cauwenberghs G, Kuzum D. Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge. Nat Commun 2024; 15:3492. [PMID: 38664381 PMCID: PMC11045755 DOI: 10.1038/s41467-024-46682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/06/2024] [Indexed: 04/28/2024] Open
Abstract
CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.
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Affiliation(s)
- Jaeseoung Park
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Ashwani Kumar
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Yucheng Zhou
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Sangheon Oh
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Jeong-Hoon Kim
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Yuhan Shi
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Soumil Jain
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Gopabandhu Hota
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Erbin Qiu
- Department of Physics, University of California San Diego, La Jolla, CA, USA
| | - Amelie L Nagle
- Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ivan K Schuller
- Department of Physics, University of California San Diego, La Jolla, CA, USA
| | - Catherine D Schuman
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA
| | - Gert Cauwenberghs
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Duygu Kuzum
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
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12
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Yu E, K GK, Saxena U, Roy K. Ferroelectric capacitors and field-effect transistors as in-memory computing elements for machine learning workloads. Sci Rep 2024; 14:9426. [PMID: 38658597 DOI: 10.1038/s41598-024-59298-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 04/09/2024] [Indexed: 04/26/2024] Open
Abstract
This study discusses the feasibility of Ferroelectric Capacitors (FeCaps) and Ferroelectric Field-Effect Transistors (FeFETs) as In-Memory Computing (IMC) elements to accelerate machine learning (ML) workloads. We conducted an exploration of device fabrication and proposed system-algorithm co-design to boost performance. A novel FeCap device, incorporating an interfacial layer (IL) andHf 0.5 Zr 0.5 O 2 (HZO), ensures a reduction in operating voltage and enhances HZO scaling while being compatible with CMOS circuits. The IL also enriches ferroelectricity and retention properties. When integrated into crossbar arrays, FeCaps and FeFETs demonstrate their effectiveness as IMC components, eliminating sneak paths and enabling selector-less operation, leading to notable improvements in energy efficiency and area utilization. However, it is worth noting that limited capacitance ratios in FeCaps introduced errors in multiply-and-accumulate (MAC) computations. The proposed co-design approach helps in mitigating these errors and achieves high accuracy in classifying the CIFAR-10 dataset, elevating it from a baseline of 10% to 81.7%. FeFETs in crossbars, with a higher on-off ratio, outperform FeCaps, and our proposed charge-based sensing scheme achieved at least an order of magnitude reduction in power consumption, compared to prevalent current-based methods.
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Affiliation(s)
- Eunseon Yu
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Gaurav Kumar K
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Utkarsh Saxena
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Kaushik Roy
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
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13
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D'Agostino S, Moro F, Torchet T, Demirağ Y, Grenouillet L, Castellani N, Indiveri G, Vianello E, Payvand M. DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays. Nat Commun 2024; 15:3446. [PMID: 38658524 PMCID: PMC11043378 DOI: 10.1038/s41467-024-47764-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 04/11/2024] [Indexed: 04/26/2024] Open
Abstract
An increasing number of studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the neocortex for supporting non-linear computation through localized synaptic integration. In particular, dendritic branches play a key role in temporal signal processing and feature detection. This is accomplished thanks to coincidence detection (CD) mechanisms enabled by the presence of synaptic delays that align temporally disparate inputs for effective integration. Computational studies on spiking neural networks further highlight the significance of delays for achieving spatio-temporal pattern recognition with pure feed-forward neural networks, without the need of resorting to recurrent architectures. In this work, we present "DenRAM", the first realization of a feed-forward spiking neural network with dendritic compartments, implemented using analog electronic circuits integrated into a 130 nm technology node and coupled with Resistive Random Access Memory (RRAM) technology. DenRAM's dendritic circuits use RRAM devices to implement both delays and synaptic weights in the network. By configuring the RRAM devices to reproduce bio-realistic timescales, and by exploiting their heterogeneity we experimentally demonstrate DenRAM's ability to replicate synaptic delay profiles, and to efficiently implement CD for spatio-temporal pattern recognition. To validate the architecture, we conduct comprehensive system-level simulations on two representative temporal benchmarks, demonstrating DenRAM's resilience to analog hardware noise, and its superior accuracy compared to recurrent architectures with an equivalent number of parameters. DenRAM not only brings rich temporal processing capabilities to neuromorphic architectures, but also reduces the memory footprint of edge devices, warrants high accuracy on temporal benchmarks, and represents a significant step-forward in low-power real-time signal processing technologies.
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Affiliation(s)
- Simone D'Agostino
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- CEA-Leti, Université Grenoble Alpes, Grenoble, France
| | - Filippo Moro
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- CEA-Leti, Université Grenoble Alpes, Grenoble, France
| | - Tristan Torchet
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yiğit Demirağ
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | | | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Melika Payvand
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
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14
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Wen TH, Hung JM, Huang WH, Jhang CJ, Lo YC, Hsu HH, Ke ZE, Chen YC, Chin YH, Su CI, Khwa WS, Lo CC, Liu RS, Hsieh CC, Tang KT, Ho MS, Chou CC, Chih YD, Chang TYJ, Chang MF. Fusion of memristor and digital compute-in-memory processing for energy-efficient edge computing. Science 2024; 384:325-332. [PMID: 38669568 DOI: 10.1126/science.adf5538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 03/19/2024] [Indexed: 04/28/2024]
Abstract
Artificial intelligence (AI) edge devices prefer employing high-capacity nonvolatile compute-in-memory (CIM) to achieve high energy efficiency and rapid wakeup-to-response with sufficient accuracy. Most previous works are based on either memristor-based CIMs, which suffer from accuracy loss and do not support training as a result of limited endurance, or digital static random-access memory (SRAM)-based CIMs, which suffer from large area requirements and volatile storage. We report an AI edge processor that uses a memristor-SRAM CIM-fusion scheme to simultaneously exploit the high accuracy of the digital SRAM CIM and the high energy-efficiency and storage density of the resistive random-access memory memristor CIM. This also enables adaptive local training to accommodate personalized characterization and user environment. The fusion processor achieved high CIM capacity, short wakeup-to-response latency (392 microseconds), high peak energy efficiency (77.64 teraoperations per second per watt), and robust accuracy (<0.5% accuracy loss). This work demonstrates that memristor technology has moved beyond in-lab development stages and now has manufacturability for AI edge processors.
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Affiliation(s)
- Tai-Hao Wen
- Taiwan Semiconductor Manufacturing Company Limited (TSMC), No. 8, Li-Hsin Rd. 6, Hsinchu Science Park, Hsinchu 300, Taiwan, R.O.C
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Je-Min Hung
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Wei-Hsing Huang
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Chuan-Jia Jhang
- Taiwan Semiconductor Manufacturing Company Limited (TSMC), No. 8, Li-Hsin Rd. 6, Hsinchu Science Park, Hsinchu 300, Taiwan, R.O.C
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Yun-Chen Lo
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Hung-Hsi Hsu
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Zhao-En Ke
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Yu-Chiao Chen
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Yu-Hsiang Chin
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Chin-I Su
- Taiwan Semiconductor Manufacturing Company Limited (TSMC), No. 8, Li-Hsin Rd. 6, Hsinchu Science Park, Hsinchu 300, Taiwan, R.O.C
| | - Win-San Khwa
- Taiwan Semiconductor Manufacturing Company Limited (TSMC), No. 8, Li-Hsin Rd. 6, Hsinchu Science Park, Hsinchu 300, Taiwan, R.O.C
| | - Chung-Chuan Lo
- Department of Life Science, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Ren-Shuo Liu
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Chih-Cheng Hsieh
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Kea-Tiong Tang
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
| | - Mon-Shu Ho
- Department of Physics, National Chung Hsing University (NCHU), No. 145, Xingda Rd., South Dist., Taichung City 402, Taiwan, R.O.C
| | - Chung-Cheng Chou
- Taiwan Semiconductor Manufacturing Company Limited (TSMC), No. 8, Li-Hsin Rd. 6, Hsinchu Science Park, Hsinchu 300, Taiwan, R.O.C
| | - Yu-Der Chih
- Taiwan Semiconductor Manufacturing Company Limited (TSMC), No. 8, Li-Hsin Rd. 6, Hsinchu Science Park, Hsinchu 300, Taiwan, R.O.C
| | - Tsung-Yung Jonathan Chang
- Taiwan Semiconductor Manufacturing Company Limited (TSMC), No. 8, Li-Hsin Rd. 6, Hsinchu Science Park, Hsinchu 300, Taiwan, R.O.C
| | - Meng-Fan Chang
- Taiwan Semiconductor Manufacturing Company Limited (TSMC), No. 8, Li-Hsin Rd. 6, Hsinchu Science Park, Hsinchu 300, Taiwan, R.O.C
- Department of Electrical Engineering, National Tsing Hua University (NTHU), No. 101, Sec. 2, Guangfu Rd., East Dist., Hsinchu City, Hsinchu 300, Taiwan, R.O.C
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15
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Fedotov M, Korotitsky V, Koveshnikov S. Modeling of Self-Aligned Selector Based on Ultra-Thin Metal Oxide for Resistive Random-Access Memory (RRAM) Crossbar Arrays. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:668. [PMID: 38668162 PMCID: PMC11054844 DOI: 10.3390/nano14080668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/05/2024] [Accepted: 04/10/2024] [Indexed: 04/29/2024]
Abstract
Resistive random-access memory (RRAM) is a crucial element for next-generation large-scale memory arrays, analogue neuromorphic computing and energy-efficient System-on-Chip applications. For these applications, RRAM elements are arranged into Crossbar arrays, where rectifying selector devices are required for correct read operation of the memory cells. One of the key advantages of RRAM is its high scalability due to the filamentary mechanism of resistive switching, as the cell conductivity is not dependent on the cell area. Thus, a selector device becomes a limiting factor in Crossbar arrays in terms of scalability, as its area exceeds the minimal possible area of an RRAM cell. We propose a tunnel diode selector, which is self-aligned with an RRAM cell and, thus, occupies the same area. In this study, we address the theoretical and modeling aspects of creating a self-aligned selector with optimal parameters to avoid any deterioration of RRAM cell performance. We investigate the possibilities of using a tunnel diode based on single- and double-layer dielectrics and determine their optimal physical properties to be used in an HfOx-based RRAM Crossbar array.
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Affiliation(s)
- Mikhail Fedotov
- Institute of Microelectronics Technology and High-Purity Materials, Russian Academy of Science (IMT RAS), 6 Academician Ossipyan Str., Moscow District, Chernogolovka 142432, Russia
| | | | - Sergei Koveshnikov
- Institute of Microelectronics Technology and High-Purity Materials, Russian Academy of Science (IMT RAS), 6 Academician Ossipyan Str., Moscow District, Chernogolovka 142432, Russia
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16
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Kim K, Song MS, Hwang H, Hwang S, Kim H. A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects. Front Neurosci 2024; 18:1279708. [PMID: 38660225 PMCID: PMC11042536 DOI: 10.3389/fnins.2024.1279708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 03/14/2024] [Indexed: 04/26/2024] Open
Abstract
A neuromorphic system is composed of hardware-based artificial neurons and synaptic devices, designed to improve the efficiency of neural computations inspired by energy-efficient and parallel operations of the biological nervous system. A synaptic device-based array can compute vector-matrix multiplication (VMM) with given input voltage signals, as a non-volatile memory device stores the weight information of the neural network in the form of conductance or capacitance. However, unlike software-based neural networks, the neuromorphic system unavoidably exhibits non-ideal characteristics that can have an adverse impact on overall system performance. In this study, the characteristics required for synaptic devices and their importance are discussed, depending on the targeted application. We categorize synaptic devices into two types: conductance-based and capacitance-based, and thoroughly explore the operations and characteristics of each device. The array structure according to the device structure and the VMM operation mechanism of each structure are analyzed, including recent advances in array-level implementation of synaptic devices. Furthermore, we reviewed studies to minimize the effect of hardware non-idealities, which degrades the performance of hardware neural networks. These studies introduce techniques in hardware and signal engineering, as well as software-hardware co-optimization, to address these non-idealities through compensation approaches.
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Affiliation(s)
- Kyuree Kim
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
| | - Min Suk Song
- Division of Nanoscale Semiconductor Engineering, Hanyang University, Seoul, Republic of Korea
| | - Hwiho Hwang
- Division of Materials Science and Engineering, Hanyang University, Seoul, Republic of Korea
| | - Sungmin Hwang
- Department of AI Semiconductor Engineering, Korea University, Sejong, Republic of Korea
| | - Hyungjin Kim
- Division of Materials Science and Engineering, Hanyang University, Seoul, Republic of Korea
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17
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Dong X, Sun H, Lai X, Yang F, Ma T, Zhang X, Chen J, Zhao Y, Chen J, Zhang X, Li Y. MoO x Synaptic Memristor with Programmable Multilevel Conductance for Reliable Neuromorphic Hardware. J Phys Chem Lett 2024; 15:3668-3676. [PMID: 38535723 DOI: 10.1021/acs.jpclett.4c00600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Memristor holds great potential for enabling next-generation neuromorphic computing hardware. Controlling the interfacial characteristics of the device is critical for seamlessly integrating and replicating the synaptic dynamic behaviors; however, it is commonly overlooked. Herein, we report the straightforward oxidation of a Mo electrode in air to design MoOx memristors that exhibit nonvolatile ultrafast switching (0.6-0.8 mV/decade, <1 mV/decade) with a high on/off ratio (>104), a long durability (>104 s), a low power consumption (17.9 μW), excellent device-to-device uniformity, ingeniously synaptic behavior, and finely programmable multilevel analog switching. The analyzed physical mechanism of the observed resistive switching behavior might be the conductive filaments formed by the oxygen vacancies. Intriguingly, upon organization into memristor-based crossbar arrays, in addition to simulated multipattern memorization, edge detection on random images can be implemented well by parallel processing of pixels using a 3 × 3 × 2 array of Prewitt filter groups. These are vital functions for neural system hardware in efficient in-memory computing neural systems with massive parallelism beyond a von Neumann architecture.
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Affiliation(s)
- Xiaofei Dong
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Hao Sun
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Xinhua Lai
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Fengxia Yang
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Tingting Ma
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Xiang Zhang
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Jianbiao Chen
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Yun Zhao
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Jiangtao Chen
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Xuqiang Zhang
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Yan Li
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
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18
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Zhang Y, Chu L, Li W. A Fully-Integrated Memristor Chip for Edge Learning. NANO-MICRO LETTERS 2024; 16:166. [PMID: 38564024 PMCID: PMC10987402 DOI: 10.1007/s40820-024-01368-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 01/26/2024] [Indexed: 04/04/2024]
Abstract
The fully-integrated memristor chip for edge learning provides a solid foundation for neural network computation. The fully-integrated memristor chip enables efficient object recognition in noisy backgrounds while minimizing energy consumption. The computing-in-memory chip represents an innovative and interdisciplinary technology that extends beyond multiple research domains.
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Affiliation(s)
- Yanhong Zhang
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China
| | - Liang Chu
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China.
| | - Wenjun Li
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China.
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19
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Duan X, Cao Z, Gao K, Yan W, Sun S, Zhou G, Wu Z, Ren F, Sun B. Memristor-Based Neuromorphic Chips. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2310704. [PMID: 38168750 DOI: 10.1002/adma.202310704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 12/15/2023] [Indexed: 01/05/2024]
Abstract
In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain-like chips, which are known for their robust processing power and energy-efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor-based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips. Subsequently, a thorough discussion of the memristor array, which serves as the pivotal component of the neuromorphic chip, as well as an examination of the present mainstream neural networks, is delved. Furthermore, the design of the neuromorphic chip is categorized into three crucial sections, including synapse-neuron cores, networks on chip (NoC), and neural network design. Finally, the key performance metrics of the chip is highlighted, as well as the key metrics related to the memristor devices are employed to realize both the synaptic and neuronal components.
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Affiliation(s)
- Xuegang Duan
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Zelin Cao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Kaikai Gao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Wentao Yan
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Siyu Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing, 400715, China
| | - Zhenhua Wu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 DongChuan Rd, Shanghai, 200240, China
| | - Fenggang Ren
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Bai Sun
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
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20
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Li XD, Chen NK, Wang BQ, Niu M, Xu M, Miao X, Li XB. Resistive Memory Devices at the Thinnest Limit: Progress and Challenges. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307951. [PMID: 38197585 DOI: 10.1002/adma.202307951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/28/2023] [Indexed: 01/11/2024]
Abstract
The Si-based integrated circuits industry has been developing for more than half a century, by focusing on the scaling-down of transistor. However, the miniaturization of transistors will soon reach its physical limits, thereby requiring novel material and device technologies. Resistive memory is a promising candidate for in-memory computing and energy-efficient synaptic devices that can satisfy the computational demands of the future applications. However, poor cycle-to-cycle and device-to-device uniformities hinder its mass production. 2D materials, as a new type of semiconductor, is successfully employed in various micro/nanoelectronic devices and have the potential to drive future innovation in resistive memory technology. This review evaluates the potential of using the thinnest advanced materials, that is, monolayer 2D materials, for memristor or memtransistor applications, including resistive switching behavior and atomic mechanism, high-frequency device performances, and in-memory computing/neuromorphic computing applications. The scaling-down advantages of promising monolayer 2D materials including graphene, transition metal dichalcogenides, and hexagonal boron nitride are presented. Finally, the technical challenges of these atomic devices for practical applications are elaborately discussed. The study of monolayer-2D-material-based resistive memory is expected to play a positive role in the exploration of beyond-Si electronic technologies.
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Affiliation(s)
- Xiao-Dong Li
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, China
| | - Nian-Ke Chen
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, China
| | - Bai-Qian Wang
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, China
| | - Meng Niu
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, China
| | - Ming Xu
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xiangshui Miao
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xian-Bin Li
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, China
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21
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Wang H, Guan Z, Li J, Luo Z, Du X, Wang Z, Zhao H, Shen S, Yin Y, Li X. Silicon-Compatible Ferroelectric Tunnel Junctions with a SiO 2/Hf 0.5Zr 0.5O 2 Composite Barrier as Low-Voltage and Ultra-High-Speed Memristors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2211305. [PMID: 38291852 DOI: 10.1002/adma.202211305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 12/19/2023] [Indexed: 02/01/2024]
Abstract
The big data era requires ultrafast, low-power, and silicon-compatible materials and devices for information storage and processing. Here, ferroelectric tunnel junctions (FTJs) based on SiO2/Hf0.5Zr0.5O2 composite barrier and both conducting electrodes are designed and fabricated on Si substrates. The FTJ achieves the fastest write speed of 500 ps under 5 V (2 orders of magnitude faster than reported silicon-compatible FTJs) or 10 ns speed at a low voltage of 1.5 V (the lowest voltage among FTJs at similar speeds), low write current density of 1.3 × 104 A cm-2, 8 discrete states, good retention > 105 s at 85 °C, and endurance > 107. In addition, it provides a large read current (88 A cm-2) at 0.1 V, 2 orders of magnitude larger than reported FTJs. Interestingly, in FTJ-based synapses, gradually tunable conductance states (128 states) with high linearity (<1) are obtained by 10 ns pulses of <1.2 V, and a high accuracy of 91.8% in recognizing fashion product images is achieved by online neural network simulations. These results highlight that silicon-compatible HfO2-based FTJs are promising for high-performance nonvolatile memories and electrical synapses.
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Affiliation(s)
- He Wang
- Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Zeyu Guan
- Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Jiachen Li
- Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Zhen Luo
- Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Xinzhe Du
- Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Zijian Wang
- Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Haoyu Zhao
- Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Shengchun Shen
- Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Yuewei Yin
- Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Xiaoguang Li
- Hefei National Research Center for Physical Sciences at the Microscale, Department of Physics and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, 230026, P. R. China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
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22
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Shi T, Zhang H, Cui S, Liu J, Gu Z, Wang Z, Yan X, Liu Q. Stochastic neuro-fuzzy system implemented in memristor crossbar arrays. SCIENCE ADVANCES 2024; 10:eadl3135. [PMID: 38517972 PMCID: PMC10959402 DOI: 10.1126/sciadv.adl3135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/16/2024] [Indexed: 03/24/2024]
Abstract
Neuro-symbolic artificial intelligence has garnered considerable attention amid increasing industry demands for high-performance neural networks that are interpretable and adaptable to previously unknown problem domains with minimal reconfiguration. However, implementing neuro-symbolic hardware is challenging due to the complexity in symbolic knowledge representation and calculation. We experimentally demonstrated a memristor-based neuro-fuzzy hardware based on TiN/TaOx/HfOx/TiN chips that is superior to its silicon-based counterpart in terms of throughput and energy efficiency by using array topological structure for knowledge representation and physical laws for computing. Intrinsic memristor variability is fully exploited to increase robustness in knowledge representation. A hybrid in situ training strategy is proposed for error minimizing in training. The hardware adapts easier to a previously unknown environment, achieving ~6.6 times faster convergence and ~6 times lower error than deep learning. The hardware energy efficiency is over two orders of magnitude greater than field-programmable gate arrays. This research greatly extends the capability of memristor-based neuromorphic computing systems in artificial intelligence.
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Affiliation(s)
- Tuo Shi
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Hui Zhang
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Shiyu Cui
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Jinchang Liu
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Zixi Gu
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Zhanfeng Wang
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding 071002, P. R. China
| | - Xiaobing Yan
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding 071002, P. R. China
| | - Qi Liu
- Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
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23
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Aguirre F, Sebastian A, Le Gallo M, Song W, Wang T, Yang JJ, Lu W, Chang MF, Ielmini D, Yang Y, Mehonic A, Kenyon A, Villena MA, Roldán JB, Wu Y, Hsu HH, Raghavan N, Suñé J, Miranda E, Eltawil A, Setti G, Smagulova K, Salama KN, Krestinskaya O, Yan X, Ang KW, Jain S, Li S, Alharbi O, Pazos S, Lanza M. Hardware implementation of memristor-based artificial neural networks. Nat Commun 2024; 15:1974. [PMID: 38438350 PMCID: PMC10912231 DOI: 10.1038/s41467-024-45670-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 02/01/2024] [Indexed: 03/06/2024] Open
Abstract
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.
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Affiliation(s)
- Fernando Aguirre
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | | | | | - Wenhao Song
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - Tong Wang
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - Wei Lu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Meng-Fan Chang
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET, Piazza L. da Vinci 32, 20133, Milano, Italy
| | - Yuchao Yang
- School of Electronic and Computer Engineering, Peking University, Shenzhen, China
| | - Adnan Mehonic
- Department of Electronic and Electrical Engineering, University College London (UCL), Torrington Place, WC1E 7JE, London, UK
| | - Anthony Kenyon
- Department of Electronic and Electrical Engineering, University College London (UCL), Torrington Place, WC1E 7JE, London, UK
| | - Marco A Villena
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Juan B Roldán
- Departamento de Electrónica y Tecnología de Computadores, Facultad de Ciencias, Universidad de Granada, Avenida Fuentenueva s/n, 18071, Granada, Spain
| | - Yuting Wu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hung-Hsi Hsu
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Nagarajan Raghavan
- Engineering Product Development (EPD) Pillar, Singapore University of Technology & Design, 8 Somapah Road, 487372, Singapore, Singapore
| | - Jordi Suñé
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | - Enrique Miranda
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | - Ahmed Eltawil
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Gianluca Setti
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Kamilya Smagulova
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Khaled N Salama
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Olga Krestinskaya
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Samarth Jain
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Osamah Alharbi
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Sebastian Pazos
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Mario Lanza
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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24
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Wang J, Ilyas N, Ren Y, Ji Y, Li S, Li C, Liu F, Gu D, Ang KW. Technology and Integration Roadmap for Optoelectronic Memristor. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307393. [PMID: 37739413 DOI: 10.1002/adma.202307393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/10/2023] [Indexed: 09/24/2023]
Abstract
Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.
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Affiliation(s)
- Jinyong Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Nasir Ilyas
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yujing Ren
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
| | - Yun Ji
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Changcun Li
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Fucai Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Deen Gu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
- Institute of Materials Research and Engineering, A*STAR, Singapore, 138634, Singapore
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25
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Kwak H, Kim N, Jeon S, Kim S, Woo J. Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing. NANO CONVERGENCE 2024; 11:9. [PMID: 38416323 PMCID: PMC10902254 DOI: 10.1186/s40580-024-00415-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 01/30/2024] [Indexed: 02/29/2024]
Abstract
Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems.
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Affiliation(s)
- Hyunjeong Kwak
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea
| | - Nayeon Kim
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea
| | - Seonuk Jeon
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea
| | - Seyoung Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.
| | - Jiyong Woo
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea.
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26
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Song W, Rao M, Li Y, Li C, Zhuo Y, Cai F, Wu M, Yin W, Li Z, Wei Q, Lee S, Zhu H, Gong L, Barnell M, Wu Q, Beerel PA, Chen MSW, Ge N, Hu M, Xia Q, Yang JJ. Programming memristor arrays with arbitrarily high precision for analog computing. Science 2024; 383:903-910. [PMID: 38386733 DOI: 10.1126/science.adi9405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 12/28/2023] [Indexed: 02/24/2024]
Abstract
In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches.
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Affiliation(s)
- Wenhao Song
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
- TetraMem Inc., Fremont, CA, USA
| | | | - Yunning Li
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - Can Li
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - Ye Zhuo
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | | | | | | | | | | | | | | | | | - Mark Barnell
- Air Force Research Lab, Information Directorate, Rome, NY, USA
| | - Qing Wu
- Air Force Research Lab, Information Directorate, Rome, NY, USA
| | - Peter A Beerel
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Mike Shuo-Wei Chen
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Ning Ge
- TetraMem Inc., Fremont, CA, USA
| | - Miao Hu
- TetraMem Inc., Fremont, CA, USA
| | - Qiangfei Xia
- TetraMem Inc., Fremont, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J Joshua Yang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
- TetraMem Inc., Fremont, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
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27
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Lee KJ, Kim JH, Jeon S, Shin CW, Kim HR, Park HG, Kim J. Polarization-Dependent Memory and Erasure in Quantum Dots/Graphene Synaptic Devices. NANO LETTERS 2024; 24:2421-2427. [PMID: 38319957 DOI: 10.1021/acs.nanolett.4c00124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
We demonstrate excitatory and inhibitory properties in a single heterostructure consisting of two quantum dots/graphene synaptic elements using linearly polarized monochromatic light. Perovskite quantum dots and PbS quantum dots were used to increase and decrease photocurrent weights, respectively. The polarization-dependent photocurrent was realized by adding a polarizer in the middle of the PbS quantum dots/graphene and perovskite quantum dots/graphene elements. When linearly polarized light passed through the polarizer, both the lower excitatory and upper inhibitory devices were activated, with the lower device with the stronger response dominating to increase the current weight. In contrast, the polarized light was blocked by the polarizer, and the above device was only operated, reducing the current weight. Furthermore, two orthogonal polarizations of light were used to perform the sequential processes of potentiation and habituation. By adjustment of the polarization angle of light, not only the direction of the current weight but also its level was altered.
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Affiliation(s)
- Ki-Jeong Lee
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
| | - Jin Hyung Kim
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
| | - Sooin Jeon
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
| | - Chi Won Shin
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul 08826, Republic of Korea
| | - Ha-Reem Kim
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul 08826, Republic of Korea
| | - Hong-Gyu Park
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul 08826, Republic of Korea
| | - Jungkil Kim
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
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28
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Park Y, Lee JH, Lee JK, Kim S. Multifunctional HfAlO thin film: Ferroelectric tunnel junction and resistive random access memory. J Chem Phys 2024; 160:074704. [PMID: 38375908 DOI: 10.1063/5.0190195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/16/2024] [Indexed: 02/21/2024] Open
Abstract
This study presents findings indicating that the ferroelectric tunnel junction (FTJ) or resistive random-access memory (RRAM) in one cell can be intentionally selected depending on the application. The HfAlO film annealed at 700 °C shows stable FTJ characteristics and can be converted into RRAM by forming a conductive filament inside the same cell, that is, the process of intentionally forming a conductive filament is the result of defect generation and redistribution, and applying compliance current prior to a hard breakdown event of the dielectric film enables subsequent RRAM operation. The converted RRAM demonstrated good memory performance. Through current-voltage fitting, it was confirmed that the two resistance states of the FTJ and RRAM had different transport mechanisms. In the RRAM, the 1/f noise power of the high-resistance state (HRS) was about ten times higher than that of the low-resistance state (LRS). This is because the noise components increase due to the additional current paths in the HRS. The 1/f noise power according to resistance states in the FTJ was exactly the opposite result from the case of the RRAM. This is because the noise component due to the Poole-Frenkel emission is added to the noise component due to the tunneling current in the LRS. In addition, we confirmed the potentiation and depression characteristics of the two devices and further evaluated the accuracy of pattern recognition through a simulation by considering a dataset from the Modified National Institute of Standards and Technology.
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Affiliation(s)
- Yongjin Park
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Jong-Ho Lee
- The Department of Electrical and Computer Engineering and Inter-University Semiconductor Research Center (ISRC), Seoul National University, Seoul 08826, South Korea
| | - Jung-Kyu Lee
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
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29
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Wan C, Pei M, Shi K, Cui H, Long H, Qiao L, Xing Q, Wan Q. Toward a Brain-Neuromorphics Interface. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311288. [PMID: 38339866 DOI: 10.1002/adma.202311288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/17/2024] [Indexed: 02/12/2024]
Abstract
Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.
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Affiliation(s)
- Changjin Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjiao Pei
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Kailu Shi
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Hangyuan Cui
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Haotian Long
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Lesheng Qiao
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qianye Xing
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qing Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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30
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Hua Q, Shen G. Low-dimensional nanostructures for monolithic 3D-integrated flexible and stretchable electronics. Chem Soc Rev 2024; 53:1316-1353. [PMID: 38196334 DOI: 10.1039/d3cs00918a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Flexible/stretchable electronics, which are characterized by their ultrathin design, lightweight structure, and excellent mechanical robustness and conformability, have garnered significant attention due to their unprecedented potential in healthcare, advanced robotics, and human-machine interface technologies. An increasing number of low-dimensional nanostructures with exceptional mechanical, electronic, and/or optical properties are being developed for flexible/stretchable electronics to fulfill the functional and application requirements of information sensing, processing, and interactive loops. Compared to the traditional single-layer format, which has a restricted design space, a monolithic three-dimensional (M3D) integrated device architecture offers greater flexibility and stretchability for electronic devices, achieving a high-level of integration to accommodate the state-of-the-art design targets, such as skin-comfort, miniaturization, and multi-functionality. Low-dimensional nanostructures possess small size, unique characteristics, flexible/elastic adaptability, and effective vertical stacking capability, boosting the advancement of M3D-integrated flexible/stretchable systems. In this review, we provide a summary of the typical low-dimensional nanostructures found in semiconductor, interconnect, and substrate materials, and discuss the design rules of flexible/stretchable devices for intelligent sensing and data processing. Furthermore, artificial sensory systems in 3D integration have been reviewed, highlighting the advancements in flexible/stretchable electronics that are deployed with high-density, energy-efficiency, and multi-functionalities. Finally, we discuss the technical challenges and advanced methodologies involved in the design and optimization of low-dimensional nanostructures, to achieve monolithic 3D-integrated flexible/stretchable multi-sensory systems.
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Affiliation(s)
- Qilin Hua
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China.
- Institute of Flexible Electronics, Beijing Institute of Technology, Beijing 102488, China
| | - Guozhen Shen
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China.
- Institute of Flexible Electronics, Beijing Institute of Technology, Beijing 102488, China
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Koo RH, Shin W, Kim S, Im J, Park SH, Ko JH, Kwon D, Kim JJ, Kwon D, Lee JH. Proposition of Adaptive Read Bias: A Solution to Overcome Power and Scaling Limitations in Ferroelectric-Based Neuromorphic System. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2303735. [PMID: 38039488 PMCID: PMC10837350 DOI: 10.1002/advs.202303735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/11/2023] [Indexed: 12/03/2023]
Abstract
Hardware neuromorphic systems are crucial for the energy-efficient processing of massive amounts of data. Among various candidates, hafnium oxide ferroelectric tunnel junctions (FTJs) are highly promising for artificial synaptic devices. However, FTJs exhibit non-ideal characteristics that introduce variations in synaptic weights, presenting a considerable challenge in achieving high-performance neuromorphic systems. The primary objective of this study is to analyze the origin and impact of these variations in neuromorphic systems. The analysis reveals that the major bottleneck in achieving a high-performance neuromorphic system is the dynamic variation, primarily caused by the intrinsic 1/f noise of the device. As the device area is reduced and the read bias (VRead ) is lowered, the intrinsic noise of the FTJs increases, presenting an inherent limitation for implementing area- and power-efficient neuromorphic systems. To overcome this limitation, an adaptive read-biasing (ARB) scheme is proposed that applies a different VRead to each layer of the neuromorphic system. By exploiting the different noise sensitivities of each layer, the ARB method demonstrates significant power savings of 61.3% and a scaling effect of 91.9% compared with conventional biasing methods. These findings contribute significantly to the development of more accurate, efficient, and scalable neuromorphic systems.
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Affiliation(s)
- Ryun-Han Koo
- Inter-University Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Wonjun Shin
- Inter-University Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Seungwhan Kim
- Inter-University Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Jiseong Im
- Inter-University Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Sung-Ho Park
- Inter-University Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Jong Hyun Ko
- Inter-University Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Dongseok Kwon
- Inter-University Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Jae-Joon Kim
- Inter-University Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Daewoong Kwon
- Department of Electrical Engineering, Hanyang University, Seoul, 04763, South Korea
| | - Jong-Ho Lee
- Inter-University Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, South Korea
- Ministry of Science and ICT, Sejong, 30109, South Korea
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32
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Iliasov AI, Matsukatova AN, Emelyanov AV, Slepov PS, Nikiruy KE, Rylkov VV. Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co-Fe-B) x(LiNbO 3) 100-x nanocomposite memristors. NANOSCALE HORIZONS 2024; 9:238-247. [PMID: 38165725 DOI: 10.1039/d3nh00421j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
MLP-Mixer based on multilayer perceptrons (MLPs) is a novel architecture of a neuromorphic computing system (NCS) introduced for image classification tasks without convolutional layers. Its software realization demonstrates high classification accuracy, although the number of trainable weights is relatively low. One more promising way of improving the NCS performance, especially in terms of power consumption, is its hardware realization using memristors. Therefore, in this work, we proposed an NCS with an adapted MLP-Mixer architecture and memristive weights. For this purpose, we used a passive crossbar array of (Co-Fe-B)x(LiNbO3)100-x memristors. Firstly, we studied the characteristics of such memristors, including their minimal resistive switching time, which was extrapolated to be in the picosecond range. Secondly, we created a fully hardware NCS with memristive weights that are capable of classification of simple 4-bit vectors. The system was shown to be robust to noise introduction in the input patterns. Finally, we used experimental memristive characteristics to simulate an adapted MLP-Mixer architecture that demonstrated a classification accuracy of (94.7 ± 0.3)% on the Modified National Institute of Standards and Technology (MNIST) dataset. The obtained results are the first steps toward the realization of memristive NCS with a promising MLP-Mixer architecture.
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Affiliation(s)
- Aleksandr I Iliasov
- National Research Centre Kurchatov Institute, 123182 Moscow, Russia.
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Anna N Matsukatova
- National Research Centre Kurchatov Institute, 123182 Moscow, Russia.
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Andrey V Emelyanov
- National Research Centre Kurchatov Institute, 123182 Moscow, Russia.
- Moscow Institute of Physics and Technology (State University), 141700 Dolgoprudny, Moscow Region, Russia
| | - Pavel S Slepov
- Steklov Mathematical Institute RAS, 119991 Moscow, Russia
| | | | - Vladimir V Rylkov
- National Research Centre Kurchatov Institute, 123182 Moscow, Russia.
- Kotelnikov Institute of Radio Engineering and Electronics RAS, 141190 Fryazino, Moscow Region, Russia
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33
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Jebali F, Majumdar A, Turck C, Harabi KE, Faye MC, Muhr E, Walder JP, Bilousov O, Michaud A, Vianello E, Hirtzlin T, Andrieu F, Bocquet M, Collin S, Querlioz D, Portal JM. Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell. Nat Commun 2024; 15:741. [PMID: 38272896 PMCID: PMC10811339 DOI: 10.1038/s41467-024-44766-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 01/04/2024] [Indexed: 01/27/2024] Open
Abstract
Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based networks rely on analog in-memory computing, necessitating a stable and precise power supply, which is incompatible with the inherently unstable and unreliable energy harvesters. In this work, we fabricated a robust binarized neural network comprising 32,768 memristors, powered by a miniature wide-bandgap solar cell optimized for edge applications. Our circuit employs a resilient digital near-memory computing approach, featuring complementarily programmed memristors and logic-in-sense-amplifier. This design eliminates the need for compensation or calibration, operating effectively under diverse conditions. Under high illumination, the circuit achieves inference performance comparable to that of a lab bench power supply. In low illumination scenarios, it remains functional with slightly reduced accuracy, seamlessly transitioning to an approximate computing mode. Through image classification neural network simulations, we demonstrate that misclassified images under low illumination are primarily difficult-to-classify cases. Our approach lays the groundwork for self-powered AI and the creation of intelligent sensors for various applications in health, safety, and environment monitoring.
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Affiliation(s)
- Fadi Jebali
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
| | - Atreya Majumdar
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
| | - Clément Turck
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
| | - Kamel-Eddine Harabi
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
| | - Mathieu-Coumba Faye
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
- Université Grenoble Alpes, CEA, LETI, Grenoble, France
| | - Eloi Muhr
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
| | - Jean-Pierre Walder
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
| | | | - Amadéo Michaud
- Institut Photovoltaïque d'Ile-de-France (IPVF), Palaiseau, France
| | | | | | | | - Marc Bocquet
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
| | - Stéphane Collin
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
- Institut Photovoltaïque d'Ile-de-France (IPVF), Palaiseau, France
| | - Damien Querlioz
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France.
| | - Jean-Michel Portal
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France.
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Wu X, Khan AI, Lee H, Hsu CF, Zhang H, Yu H, Roy N, Davydov AV, Takeuchi I, Bao X, Wong HSP, Pop E. Novel nanocomposite-superlattices for low energy and high stability nanoscale phase-change memory. Nat Commun 2024; 15:13. [PMID: 38253559 PMCID: PMC10803317 DOI: 10.1038/s41467-023-42792-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 10/20/2023] [Indexed: 01/24/2024] Open
Abstract
Data-centric applications are pushing the limits of energy-efficiency in today's computing systems, including those based on phase-change memory (PCM). This technology must achieve low-power and stable operation at nanoscale dimensions to succeed in high-density memory arrays. Here we use a novel combination of phase-change material superlattices and nanocomposites (based on Ge4Sb6Te7), to achieve record-low power density ≈ 5 MW/cm2 and ≈ 0.7 V switching voltage (compatible with modern logic processors) in PCM devices with the smallest dimensions to date (≈ 40 nm) for a superlattice technology on a CMOS-compatible substrate. These devices also simultaneously exhibit low resistance drift with 8 resistance states, good endurance (≈ 2 × 108 cycles), and fast switching (≈ 40 ns). The efficient switching is enabled by strong heat confinement within the superlattice materials and the nanoscale device dimensions. The microstructural properties of the Ge4Sb6Te7 nanocomposite and its high crystallization temperature ensure the fast-switching speed and stability in our superlattice PCM devices. These results re-establish PCM technology as one of the frontrunners for energy-efficient data storage and computing.
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Affiliation(s)
- Xiangjin Wu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Asir Intisar Khan
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Hengyuan Lee
- Corporate Research, Taiwan Semiconductor Manufacturing Company (TSMC), Hsinchu, Taiwan
| | - Chen-Feng Hsu
- Corporate Research, Taiwan Semiconductor Manufacturing Company (TSMC), Hsinchu, Taiwan
| | - Huairuo Zhang
- Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
- Theiss Research, Inc., La Jolla, CA, USA
| | - Heshan Yu
- Department of Materials Science and Engineering, University of Maryland, College Park, MD, USA
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Neel Roy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Albert V Davydov
- Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Ichiro Takeuchi
- Department of Materials Science and Engineering, University of Maryland, College Park, MD, USA
| | - Xinyu Bao
- Corporate Research, Taiwan Semiconductor Manufacturing Company (TSMC), San Jose, CA, USA
| | - H-S Philip Wong
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Eric Pop
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
- Department of Materials Science & Engineering, Stanford University, Stanford, CA, USA.
- Precourt Institute for Energy, Stanford University, Stanford, CA, USA.
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35
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Muthu C, Resmi AN, Ajayakumar A, Ravindran NEA, Dayal G, Jinesh KB, Szaciłowski K, Vijayakumar C. Self-Assembly of Delta-Formamidinium Lead Iodide Nanoparticles to Nanorods: Study of Memristor Properties and Resistive Switching Mechanism. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2304787. [PMID: 38243886 DOI: 10.1002/smll.202304787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 12/02/2023] [Indexed: 01/22/2024]
Abstract
In the quest for advanced memristor technologies, this study introduces the synthesis of delta-formamidinium lead iodide (δ-FAPbI3 ) nanoparticles (NPs) and their self-assembly into nanorods (NRs). The formation of these NRs is facilitated by iodide vacancies, promoting the fusion of individual NPs at higher concentrations. Notably, these NRs exhibit robust stability under ambient conditions, a distinctive advantage attributed to the presence of capping ligands and a crystal lattice structured around face-sharing octahedra. When employed as the active layer in resistive random-access memory devices, these NRs demonstrate exceptional bipolar switching properties. A remarkable on/off ratio (105 ) is achieved, surpassing the performances of previously reported low-dimensional perovskite derivatives and α-FAPbI3 NP-based devices. This enhanced performance is attributed to the low off-state current owing to the reduced number of halide vacancies, intrinsic low dimensionality, and the parallel alignment of NRs on the FTO substrate. This study not only provides significant insights into the development of superior materials for memristor applications but also opens new avenues for exploring low-dimensional perovskite derivatives in advanced electronic devices.
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Affiliation(s)
- Chinnadurai Muthu
- Chemical Sciences and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, 695 019, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201 002, India
| | - A N Resmi
- Department of Physics, Indian Institute of Space Science and Technology (IIST), Thiruvananthapuram, 695 547, India
| | - Avija Ajayakumar
- Chemical Sciences and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, 695 019, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201 002, India
| | - N E Aswathi Ravindran
- Chemical Sciences and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, 695 019, India
| | - G Dayal
- Department of Physics, Indian Institute of Space Science and Technology (IIST), Thiruvananthapuram, 695 547, India
| | - K B Jinesh
- Department of Physics, Indian Institute of Space Science and Technology (IIST), Thiruvananthapuram, 695 547, India
| | - Konrad Szaciłowski
- Academic Centre for Materials and Nanotechnology, AGH University of Krakow, Mickiewicza 30, Krakow, 30 059, Poland
| | - Chakkooth Vijayakumar
- Chemical Sciences and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, 695 019, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201 002, India
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36
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Jeon K, Ryu JJ, Im S, Seo HK, Eom T, Ju H, Yang MK, Jeong DS, Kim GH. Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators. Nat Commun 2024; 15:129. [PMID: 38167379 PMCID: PMC10761713 DOI: 10.1038/s41467-023-44620-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
Memristor-integrated passive crossbar arrays (CAs) could potentially accelerate neural network (NN) computations, but studies on these devices are limited to software-based simulations owing to their poor reliability. Herein, we propose a self-rectifying memristor-based 1 kb CA as a hardware accelerator for NN computations. We conducted fully hardware-based single-layer NN classification tasks involving the Modified National Institute of Standards and Technology database using the developed passive CA, and achieved 100% classification accuracy for 1500 test sets. We also investigated the influences of the defect-tolerance capability of the CA, impact of the conductance range of the integrated memristors, and presence or absence of selection functionality in the integrated memristors on the image classification tasks. We offer valuable insights into the behavior and performance of CA devices under various conditions and provide evidence of the practicality of memristor-integrated passive CAs as hardware accelerators for NN applications.
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Affiliation(s)
- Kanghyeok Jeon
- Division of Materials Science and Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Division of Advanced Materials, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea
| | - Jin Joo Ryu
- Division of Advanced Materials, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea
- Department of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Seongil Im
- Center for Opto-Electronic Materials and Devices, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Hyun Kyu Seo
- Intelligent Electronic Device Lab, Sahmyook University, 815 Hwarang-ro, Nowon-Gu, Seoul, 01795, Republic of Korea
| | - Taeyong Eom
- Division of Advanced Materials, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea
| | - Hyunsu Ju
- Center for Opto-Electronic Materials and Devices, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea.
| | - Min Kyu Yang
- Intelligent Electronic Device Lab, Sahmyook University, 815 Hwarang-ro, Nowon-Gu, Seoul, 01795, Republic of Korea.
| | - Doo Seok Jeong
- Division of Materials Science and Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
| | - Gun Hwan Kim
- Department of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
- Department of System Semiconductor Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
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Dalgaty T, Moro F, Demirağ Y, De Pra A, Indiveri G, Vianello E, Payvand M. Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems. Nat Commun 2024; 15:142. [PMID: 38167293 PMCID: PMC10761708 DOI: 10.1038/s41467-023-44365-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
The brain's connectivity is locally dense and globally sparse, forming a small-world graph-a principle prevalent in the evolution of various species, suggesting a universal solution for efficient information routing. However, current artificial neural network circuit architectures do not fully embrace small-world neural network models. Here, we present the neuromorphic Mosaic: a non-von Neumann systolic architecture employing distributed memristors for in-memory computing and in-memory routing, efficiently implementing small-world graph topologies for Spiking Neural Networks (SNNs). We've designed, fabricated, and experimentally demonstrated the Mosaic's building blocks, using integrated memristors with 130 nm CMOS technology. We show that thanks to enforcing locality in the connectivity, routing efficiency of Mosaic is at least one order of magnitude higher than other SNN hardware platforms. This is while Mosaic achieves a competitive accuracy in a variety of edge benchmarks. Mosaic offers a scalable approach for edge systems based on distributed spike-based computing and in-memory routing.
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Affiliation(s)
| | - Filippo Moro
- CEA, LETI, Université Grenoble Alpes, Grenoble, France
| | - Yiğit Demirağ
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Melika Payvand
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
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Ren SG, Dong AW, Yang L, Xue YB, Li JC, Yu YJ, Zhou HJ, Zuo WB, Li Y, Cheng WM, Miao XS. Self-Rectifying Memristors for Three-Dimensional In-Memory Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307218. [PMID: 37972344 DOI: 10.1002/adma.202307218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/13/2023] [Indexed: 11/19/2023]
Abstract
Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence. In-memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor is developed rapidly and performs well, the sneak path issue is a critical and unavoidable challenge prevalent in large-scale and high-density crossbar arrays, particularly in three-dimensional (3D) integration. As a perfect solution to the sneak-path issue, a self-rectifying memristor (SRM) is proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (aJ level) and scalability (>102 Mbit). Moreover, SRM-configured 3D integration is considered an ideal hardware platform for 3D IMC. This review focuses on the progress in SRMs and their applications in 3D memory, IMC, neuromorphic computing, and hardware security. The advantages, disadvantages, and optimization strategies of SRMs in diverse application scenarios are illustrated. Challenges posed by physical mechanisms, fabrication processes, and peripheral circuits, as well as potential solutions at the device and system levels, are also discussed.
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Affiliation(s)
- Sheng-Guang Ren
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - A-Wei Dong
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Ling Yang
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yi-Bai Xue
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jian-Cong Li
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yin-Jie Yu
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hou-Ji Zhou
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Wen-Bin Zuo
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yi Li
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
| | - Wei-Ming Cheng
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
| | - Xiang-Shui Miao
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
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39
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Wang S, Luo Y, Zuo P, Pan L, Li Y, Sun Z. In-memory analog solution of compressed sensing recovery in one step. SCIENCE ADVANCES 2023; 9:eadj2908. [PMID: 38091396 PMCID: PMC10848716 DOI: 10.1126/sciadv.adj2908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 11/13/2023] [Indexed: 02/12/2024]
Abstract
Modern analog computing, by gaining momentum from nonvolatile resistive memory devices, deals with matrix computations. In-memory analog computing has been demonstrated for solving some basic but ordinary matrix problems in one step. Among the more complicated matrix problems, compressed sensing (CS) is a prominent example, whose recovery algorithms feature high-order matrix operations and hardware-unfriendly nonlinear functions. In light of the local competitive algorithm (LCA), here, we present a closed-loop, continuous-time resistive memory circuit for solving CS recovery in one step. Recovery of one-dimensional (1D) sparse signal and 2D compressive images has been experimentally demonstrated, showing elapsed times around few microseconds and normalized mean squared errors of 10-2. The LCA circuit is one or two orders of magnitude faster than conventional digital approaches. It also substantially outperforms other (electronic or exotically photonic) analog CS recovery methods in terms of speed, energy, and fidelity, thus representing a highly promising technology for real-time CS applications.
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Affiliation(s)
- Shiqing Wang
- School of Integrated Circuits, Institute for Artificial Intelligence and Beijing Advanced Innovation Center for Integrated Circuits, Peking University, Beijing 100871, China
| | - Yubiao Luo
- School of Integrated Circuits, Institute for Artificial Intelligence and Beijing Advanced Innovation Center for Integrated Circuits, Peking University, Beijing 100871, China
| | - Pushen Zuo
- School of Integrated Circuits, Institute for Artificial Intelligence and Beijing Advanced Innovation Center for Integrated Circuits, Peking University, Beijing 100871, China
| | - Lunshuai Pan
- School of Integrated Circuits, Institute for Artificial Intelligence and Beijing Advanced Innovation Center for Integrated Circuits, Peking University, Beijing 100871, China
| | - Yongxiang Li
- School of Integrated Circuits, Institute for Artificial Intelligence and Beijing Advanced Innovation Center for Integrated Circuits, Peking University, Beijing 100871, China
| | - Zhong Sun
- School of Integrated Circuits, Institute for Artificial Intelligence and Beijing Advanced Innovation Center for Integrated Circuits, Peking University, Beijing 100871, China
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Xu M, Chen X, Guo Y, Wang Y, Qiu D, Du X, Cui Y, Wang X, Xiong J. Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301063. [PMID: 37285592 DOI: 10.1002/adma.202301063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/15/2023] [Indexed: 06/09/2023]
Abstract
Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.
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Affiliation(s)
- Minyi Xu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinrui Chen
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yehao Guo
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yang Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dong Qiu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinchuan Du
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yi Cui
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jie Xiong
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
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Yan X, Zheng Z, Sangwan VK, Qian JH, Wang X, Liu SE, Watanabe K, Taniguchi T, Xu SY, Jarillo-Herrero P, Ma Q, Hersam MC. Moiré synaptic transistor with room-temperature neuromorphic functionality. Nature 2023; 624:551-556. [PMID: 38123805 DOI: 10.1038/s41586-023-06791-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 10/26/2023] [Indexed: 12/23/2023]
Abstract
Moiré quantum materials host exotic electronic phenomena through enhanced internal Coulomb interactions in twisted two-dimensional heterostructures1-4. When combined with the exceptionally high electrostatic control in atomically thin materials5-8, moiré heterostructures have the potential to enable next-generation electronic devices with unprecedented functionality. However, despite extensive exploration, moiré electronic phenomena have thus far been limited to impractically low cryogenic temperatures9-14, thus precluding real-world applications of moiré quantum materials. Here we report the experimental realization and room-temperature operation of a low-power (20 pW) moiré synaptic transistor based on an asymmetric bilayer graphene/hexagonal boron nitride moiré heterostructure. The asymmetric moiré potential gives rise to robust electronic ratchet states, which enable hysteretic, non-volatile injection of charge carriers that control the conductance of the device. The asymmetric gating in dual-gated moiré heterostructures realizes diverse biorealistic neuromorphic functionalities, such as reconfigurable synaptic responses, spatiotemporal-based tempotrons and Bienenstock-Cooper-Munro input-specific adaptation. In this manner, the moiré synaptic transistor enables efficient compute-in-memory designs and edge hardware accelerators for artificial intelligence and machine learning.
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Affiliation(s)
- Xiaodong Yan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Zhiren Zheng
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Vinod K Sangwan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Justin H Qian
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Xueqiao Wang
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephanie E Liu
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Kenji Watanabe
- Research Center for Functional Materials, National Institute for Materials Science, Tsukuba, Japan
| | - Takashi Taniguchi
- International Center for Material Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Japan
| | - Su-Yang Xu
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | | | - Qiong Ma
- Department of Physics, Boston College, Chestnut Hill, MA, USA.
- CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, Ontario, Canada.
| | - Mark C Hersam
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA.
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
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42
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Minnekhanov A, Matsukatova A, Trofimov A, Nesmelov A, Zavyalov S, Demin V, Emelyanov A. Reliable Memristive Synapses Based on Parylene-MoO x Nanocomposites for Neuromorphic Applications. ACS APPLIED MATERIALS & INTERFACES 2023; 15:54996-55008. [PMID: 37962902 DOI: 10.1021/acsami.3c13956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge. In this study, we report the development and comprehensive characterization of memristive devices based on a parylene-MoOx (PPX-Mo) nanocomposite layer, which exhibit improved characteristics over their parylene-based counterparts: lower switching voltage and energy, smaller dispersion, and better resistive plasticity. A robust statistical analysis identified the optimal synthesis parameters for these devices, providing valuable insights for future device optimization. The most probable resistive switching mechanism of the devices is proposed. By successfully integrating these memristors into a neuromorphic computing model and showcasing their scalability in crossbar geometry, we demonstrate their potential as functional artificial synapses. The results obtained from this study can be useful for the development of hardware-brain-inspired computational systems.
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Affiliation(s)
| | - Anna Matsukatova
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Lomonosov Moscow State University, Moscow 119991, Russia
| | - Andrey Trofimov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | | | - Sergey Zavyalov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
| | - Vyacheslav Demin
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | - Andrey Emelyanov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
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Bonnet D, Hirtzlin T, Majumdar A, Dalgaty T, Esmanhotto E, Meli V, Castellani N, Martin S, Nodin JF, Bourgeois G, Portal JM, Querlioz D, Vianello E. Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks. Nat Commun 2023; 14:7530. [PMID: 37985669 PMCID: PMC10661910 DOI: 10.1038/s41467-023-43317-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023] Open
Abstract
Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilistic nature, they are computationally intensive. An innovative solution utilizes memristors' inherent probabilistic nature to implement Bayesian neural networks. However, when using memristors, statistical effects follow the laws of device physics, whereas in Bayesian neural networks, those effects can take arbitrary shapes. This work overcome this difficulty by adopting a variational inference training augmented by a "technological loss", incorporating memristor physics. This technique enabled programming a Bayesian neural network on 75 crossbar arrays of 1,024 memristors, incorporating CMOS periphery for in-memory computing. The experimental neural network classified heartbeats with high accuracy, and estimated the certainty of its predictions. The results reveal orders-of-magnitude improvement in inference energy efficiency compared to a microcontroller or an embedded graphics processing unit performing the same task.
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Affiliation(s)
- Djohan Bonnet
- Université Grenoble Alpes, CEA, LETI, Grenoble, France.
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France.
| | | | - Atreya Majumdar
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
| | | | | | | | | | - Simon Martin
- Université Grenoble Alpes, CEA, LETI, Grenoble, France
| | | | | | - Jean-Michel Portal
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
| | - Damien Querlioz
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France.
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44
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Li B, Zhang S, Xu L, Su Q, Du B. Emerging Robust Polymer Materials for High-Performance Two-Terminal Resistive Switching Memory. Polymers (Basel) 2023; 15:4374. [PMID: 38006098 PMCID: PMC10675020 DOI: 10.3390/polym15224374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/07/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Facing the era of information explosion and the advent of artificial intelligence, there is a growing demand for information technologies with huge storage capacity and efficient computer processing. However, traditional silicon-based storage and computing technology will reach their limits and cannot meet the post-Moore information storage requirements of ultrasmall size, ultrahigh density, flexibility, biocompatibility, and recyclability. As a response to these concerns, polymer-based resistive memory materials have emerged as promising candidates for next-generation information storage and neuromorphic computing applications, with the advantages of easy molecular design, volatile and non-volatile storage, flexibility, and facile fabrication. Herein, we first summarize the memory device structures, memory effects, and memory mechanisms of polymers. Then, the recent advances in polymer resistive switching materials, including single-component polymers, polymer mixtures, 2D covalent polymers, and biomacromolecules for resistive memory devices, are highlighted. Finally, the challenges and future prospects of polymer memory materials and devices are discussed. Advances in polymer-based memristors will open new avenues in the design and integration of high-performance switching devices and facilitate their application in future information technology.
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Affiliation(s)
- Bixin Li
- School of Physics and Chemistry, Hunan First Normal University, Changsha 410205, China; (B.L.)
- Shaanxi Institute of Flexible Electronics (SIFE), Northwestern Polytechnical University (NPU), Xi’an 710072, China
- School of Physics, Central South University, 932 South Lushan Road, Changsha 410083, China
| | - Shiyang Zhang
- School of Physics and Chemistry, Hunan First Normal University, Changsha 410205, China; (B.L.)
| | - Lan Xu
- School of Physics and Chemistry, Hunan First Normal University, Changsha 410205, China; (B.L.)
| | - Qiong Su
- School of Physics and Chemistry, Hunan First Normal University, Changsha 410205, China; (B.L.)
| | - Bin Du
- School of Materials Science and Engineering, Xi’an Polytechnic University, Xi’an 710048, China
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45
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Li Y, Tang J, Gao B, Yao J, Fan A, Yan B, Yang Y, Xi Y, Li Y, Li J, Sun W, Du Y, Liu Z, Zhang Q, Qiu S, Li Q, Qian H, Wu H. Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning. Nat Commun 2023; 14:7140. [PMID: 37932300 PMCID: PMC10628152 DOI: 10.1038/s41467-023-42981-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 10/25/2023] [Indexed: 11/08/2023] Open
Abstract
In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta2O5-based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications.
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Affiliation(s)
- Yijun Li
- School of Integrated Circuits, Tsinghua University, Beijing, China
| | - Jianshi Tang
- School of Integrated Circuits, Tsinghua University, Beijing, China.
- Beijing Advanced Innovation Center for Integrated Circuits, Tsinghua University, Beijing, China.
| | - Bin Gao
- School of Integrated Circuits, Tsinghua University, Beijing, China
- Beijing Advanced Innovation Center for Integrated Circuits, Tsinghua University, Beijing, China
| | - Jian Yao
- Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Science, Suzhou, China
| | - Anjunyi Fan
- Institute for Artificial Intelligence, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China
| | - Bonan Yan
- Institute for Artificial Intelligence, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China
| | - Yuchao Yang
- Institute for Artificial Intelligence, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China
- School of Electronic and Computer Engineering, Peking University, Shenzhen, China
- Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, China
| | - Yue Xi
- School of Integrated Circuits, Tsinghua University, Beijing, China
| | - Yuankun Li
- School of Integrated Circuits, Tsinghua University, Beijing, China
| | - Jiaming Li
- School of Integrated Circuits, Tsinghua University, Beijing, China
| | - Wen Sun
- School of Integrated Circuits, Tsinghua University, Beijing, China
| | - Yiwei Du
- School of Integrated Circuits, Tsinghua University, Beijing, China
| | - Zhengwu Liu
- School of Integrated Circuits, Tsinghua University, Beijing, China
| | - Qingtian Zhang
- School of Integrated Circuits, Tsinghua University, Beijing, China
- Beijing Advanced Innovation Center for Integrated Circuits, Tsinghua University, Beijing, China
| | - Song Qiu
- Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Science, Suzhou, China
| | - Qingwen Li
- Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Science, Suzhou, China
| | - He Qian
- School of Integrated Circuits, Tsinghua University, Beijing, China
- Beijing Advanced Innovation Center for Integrated Circuits, Tsinghua University, Beijing, China
| | - Huaqiang Wu
- School of Integrated Circuits, Tsinghua University, Beijing, China.
- Beijing Advanced Innovation Center for Integrated Circuits, Tsinghua University, Beijing, China.
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46
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Wu Y, Wang Q, Wang Z, Wang X, Ayyagari B, Krishnan S, Chudzik M, Lu WD. Bulk-Switching Memristor-Based Compute-In-Memory Module for Deep Neural Network Training. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2305465. [PMID: 37747134 DOI: 10.1002/adma.202305465] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/12/2023] [Indexed: 09/26/2023]
Abstract
The constant drive to achieve higher performance in deep neural networks (DNNs) has led to the proliferation of very large models. Model training, however, requires intensive computation time and energy. Memristor-based compute-in-memory (CIM) modules can perform vector-matrix multiplication (VMM) in place and in parallel, and have shown great promises in DNN inference applications. However, CIM-based model training faces challenges due to non-linear weight updates, device variations, and low-precision. In this work, a mixed-precision training scheme is experimentally implemented to mitigate these effects using a bulk-switching memristor-based CIM module. Low-precision CIM modules are used to accelerate the expensive VMM operations, with high-precision weight updates accumulated in digital units. Memristor devices are only changed when the accumulated weight update value exceeds a pre-defined threshold. The proposed scheme is implemented with a system-onchip of fully integrated analog CIM modules and digital sub-systems, showing fast convergence of LeNet training to 97.73%. The efficacy of training larger models is evaluated using realistic hardware parameters and verifies that CIM modules can enable efficient mix-precision DNN training with accuracy comparable to full-precision software-trained models. Additionally, models trained on chip are inherently robust to hardware variations, allowing direct mapping to CIM inference chips without additional re-training.
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Affiliation(s)
- Yuting Wu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Qiwen Wang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ziyu Wang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Xinxin Wang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | | | | | - Wei D Lu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
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Modha DS, Akopyan F, Andreopoulos A, Appuswamy R, Arthur JV, Cassidy AS, Datta P, DeBole MV, Esser SK, Otero CO, Sawada J, Taba B, Amir A, Bablani D, Carlson PJ, Flickner MD, Gandhasri R, Garreau GJ, Ito M, Klamo JL, Kusnitz JA, McClatchey NJ, McKinstry JL, Nakamura Y, Nayak TK, Risk WP, Schleupen K, Shaw B, Sivagnaname J, Smith DF, Terrizzano I, Ueda T. Neural inference at the frontier of energy, space, and time. Science 2023; 382:329-335. [PMID: 37856600 DOI: 10.1126/science.adh1174] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 09/01/2023] [Indexed: 10/21/2023]
Abstract
Computing, since its inception, has been processor-centric, with memory separated from compute. Inspired by the organic brain and optimized for inorganic silicon, NorthPole is a neural inference architecture that blurs this boundary by eliminating off-chip memory, intertwining compute with memory on-chip, and appearing externally as an active memory chip. NorthPole is a low-precision, massively parallel, densely interconnected, energy-efficient, and spatial computing architecture with a co-optimized, high-utilization programming model. On the ResNet50 benchmark image classification network, relative to a graphics processing unit (GPU) that uses a comparable 12-nanometer technology process, NorthPole achieves a 25 times higher energy metric of frames per second (FPS) per watt, a 5 times higher space metric of FPS per transistor, and a 22 times lower time metric of latency. Similar results are reported for the Yolo-v4 detection network. NorthPole outperforms all prevalent architectures, even those that use more-advanced technology processes.
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48
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Chen P, Liu F, Lin P, Li P, Xiao Y, Zhang B, Pan G. Open-loop analog programmable electrochemical memory array. Nat Commun 2023; 14:6184. [PMID: 37794039 PMCID: PMC10550916 DOI: 10.1038/s41467-023-41958-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 09/21/2023] [Indexed: 10/06/2023] Open
Abstract
Emerging memories have been developed as new physical infrastructures for hosting neural networks owing to their low-power analog computing characteristics. However, accurately and efficiently programming devices in an analog-valued array is still largely limited by the intrinsic physical non-idealities of the devices, thus hampering their applications in in-situ training of neural networks. Here, we demonstrate a passive electrochemical memory (ECRAM) array with many important characteristics necessary for accurate analog programming. Different image patterns can be open-loop and serially programmed into our ECRAM array, achieving high programming accuracies without any feedback adjustments. The excellent open-loop analog programmability has led us to in-situ train a bilayer neural network and reached software-like classification accuracy of 99.4% to detect poisonous mushrooms. The training capability is further studied in simulation for large-scale neural networks such as VGG-8. Our results present a new solution for implementing learning functions in an artificial intelligence hardware using emerging memories.
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Affiliation(s)
- Peng Chen
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Fenghao Liu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Peng Lin
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China.
| | - Peihong Li
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Yu Xiao
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Bihua Zhang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Gang Pan
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China.
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49
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Jiang M, Shan K, He C, Li C. Efficient combinatorial optimization by quantum-inspired parallel annealing in analogue memristor crossbar. Nat Commun 2023; 14:5927. [PMID: 37739944 PMCID: PMC10516914 DOI: 10.1038/s41467-023-41647-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/11/2023] [Indexed: 09/24/2023] Open
Abstract
Combinatorial optimization problems are prevalent in various fields, but obtaining exact solutions remains challenging due to the combinatorial explosion with increasing problem size. Special-purpose hardware such as Ising machines, particularly memristor-based analog Ising machines, have emerged as promising solutions. However, existing simulate-annealing-based implementations have not fully exploited the inherent parallelism and analog storage/processing features of memristor crossbar arrays. This work proposes a quantum-inspired parallel annealing method that enables full parallelism and improves solution quality, resulting in significant speed and energy improvement when implemented in analog memristor crossbars. We experimentally solved tasks, including unweighted and weighted Max-Cut and traveling salesman problem, using our integrated memristor chip. The quantum-inspired parallel annealing method implemented in memristor-based hardware has demonstrated significant improvements in time- and energy-efficiency compared to previously reported simulated annealing and Ising machine implemented on other technologies. This is because our approach effectively exploits the natural parallelism, analog conductance states, and all-to-all connection provided by memristor technology, promising its potential for solving complex optimization problems with greater efficiency.
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Affiliation(s)
- Mingrui Jiang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Keyi Shan
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Chengping He
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Can Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
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50
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Yang F, Wei W, Dong X, Zhao Y, Chen J, Chen J, Zhang X, Li Y. Optoelectronic bio-synaptic plasticity on neotype kesterite memristor with switching ratio >104. J Chem Phys 2023; 159:114701. [PMID: 37712793 DOI: 10.1063/5.0167187] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023] Open
Abstract
Optoelectronic memristors hold the most potential for realizing next-generation neuromorphic computation; however, memristive devices that can integrate excellent resistive switching and both electrical-/light-induced bio-synaptic behaviors are still challenging to develop. In this study, an artificial optoelectronic synapse is proposed and realized using a kesterite-based memristor with Cu2ZnSn(S,Se)4 (CZTSSe) as the switching material and Mo/Ag as the back/top electrode. Benefiting from unique electrical features and a bi-layered structure of CZTSSe, the memristor exhibits highly stable nonvolatile resistive switching with excellent spatial uniformity, concentrated Set/Reset voltage distribution (variation <0.08/0.02 V), high On/Off ratio (>104), and long retention time (>104 s). A possible mechanism of the switching behavior in such a device is proposed. Furthermore, these memristors successfully achieve essential bio-synaptic functions under both electrical and various visible light (470-655 nm) stimulations, including electrical-induced excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, long-term depression, spike-timing-dependent plasticity, as well as light-stimulated short-/long-term plasticity and learning-forgetting-relearning process. As such, the proposed neotype kesterite-based memristor demonstrates significant potential in facilitating artificial optoelectronic synapses and enabling neuromorphic computation.
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Affiliation(s)
- Fengxia Yang
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Wenbin Wei
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Xiaofei Dong
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Yun Zhao
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Jiangtao Chen
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Jianbiao Chen
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Xuqiang Zhang
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Yan Li
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
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