1
|
Liu S, Wu Z, He Z, Chen W, Zhong X, Guo B, Liu S, Duan H, Guo Y, Zeng J, Liu G. Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception. ACS APPLIED MATERIALS & INTERFACES 2024; 16:22303-22311. [PMID: 38626428 DOI: 10.1021/acsami.4c04398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.
Collapse
Affiliation(s)
- Sixian Liu
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Zhixin Wu
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Zhilong He
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Weilin Chen
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Xiaolong Zhong
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Bingjie Guo
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Shuzhi Liu
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Hongxiao Duan
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Yanbo Guo
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Jianmin Zeng
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Gang Liu
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| |
Collapse
|
2
|
Jeon YR, Akinwande D, Choi C. Volatile threshold switching and synaptic properties controlled by Ag diffusion using Schottky defects. NANOSCALE HORIZONS 2024; 9:853-862. [PMID: 38505960 DOI: 10.1039/d3nh00571b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
We investigated diffusion memristors in the structure of Ag/Ta2O5/HfO2/Pt, in which active Ag ions control active metal ion diffusion and mimic biological brain functions. The CMOS compatible high-k metal oxide could control an Ag electrode that was ionized by applying an appropriate voltage to form a conductive filament, and the movement of Ag ions was chemically and electrically controlled due to oxygen density. This diffusion memristor exhibited diffused characteristics with a selectivity of 109, and achieved a low power consumption of 2 mW at a SET voltage of 0.2 V. The threshold transitions were reliably repeatable over 20 cycles for compliance currents of 10-6 A, 10-4 A, and no compliance current, with the largest standard deviation value of SET variation being 0.028. Upon filament formation, Ag ions readily diffused into the interface of the Ta2O5 and HfO2 layer, which was verified by investigating the Ag atomic percentage using XPS and vertical EDX and by measuring the relaxation time of 0.8 ms. Verified volatile switching device demonstrated the biological synaptic properties of quantum conductance, short-term memory, and long-term memory due to controlling the Ag. Diffusion memristors using designed control and switching layers as following film density and oxygen vacancy have improved results as low-power devices and neuromorphic devices compared to other diffusion-based devices, and these properties can be used for various applications such as selectors, synapses, and neuromorphic devices.
Collapse
Affiliation(s)
- Yu-Rim Jeon
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Deji Akinwande
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Changhwan Choi
- Division of Materials Science and Engineering, Hanyang University, Seoul 04763, Korea.
| |
Collapse
|
3
|
Sung J, Chung S, Jang Y, Jang H, Kim J, Lee C, Lee D, Jeong D, Cho K, Kim YS, Kang J, Lee W, Lee E. Unveiling the Role of Side Chain for Improving Nonvolatile Characteristics of Conjugated Polymers-Based Artificial Synapse. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400304. [PMID: 38408158 DOI: 10.1002/advs.202400304] [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: 01/09/2024] [Indexed: 02/28/2024]
Abstract
Interest has grown in services that consume a significant amount of energy, such as large language models (LLMs), and research is being conducted worldwide on synaptic devices for neuromorphic hardware. However, various complex processes are problematic for the implementation of synaptic properties. Here, synaptic characteristics are implemented through a novel method, namely side chain control of conjugated polymers. The developed devices exhibit the characteristics of the biological brain, especially spike-timing-dependent plasticity (STDP), high-pass filtering, and long-term potentiation/depression (LTP/D). Moreover, the fabricated synaptic devices show enhanced nonvolatile characteristics, such as long retention time (≈102 s), high ratio of Gmax/Gmin, high linearity, and reliable cyclic endurance (≈103 pulses). This study presents a new pathway for next-generation neuromorphic computing by modulating conjugated polymers with side chain control, thereby achieving high-performance synaptic properties.
Collapse
Affiliation(s)
- Junho Sung
- Department of Chemical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Sein Chung
- Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
| | - Yongchan Jang
- Department of Polymer Science and Engineering, Department of Energy Engineering Convergence, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Hyoik Jang
- Department of Chemical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Jiyeon Kim
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea
| | - Chan Lee
- Department of Chemical and Biological Engineering, and Institute of Chemical Processes, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Donghwa Lee
- Department of Chemical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Dongyeong Jeong
- Department of Chemical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Kilwon Cho
- Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
| | - Youn Sang Kim
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Chemical and Biological Engineering, and Institute of Chemical Processes, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Advanced Institute of Convergence Technology, Suwon, 16229, Republic of Korea
| | - Joonhee Kang
- Department of Nanoenergy Engineering, Pusan National University, Busan, 46241, Republic of Korea
| | - Wonho Lee
- Department of Polymer Science and Engineering, Department of Energy Engineering Convergence, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Eunho Lee
- Department of Chemical and Biomolecular Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea
| |
Collapse
|
4
|
Roldán JB, Cantudo A, Maldonado D, Aguilera-Pedregosa C, Moreno E, Swoboda T, Jiménez-Molinos F, Yuan Y, Zhu K, Lanza M, Muñoz Rojo M. Thermal Compact Modeling and Resistive Switching Analysis in Titanium Oxide-Based Memristors. ACS APPLIED ELECTRONIC MATERIALS 2024; 6:1424-1433. [PMID: 38435806 PMCID: PMC10903745 DOI: 10.1021/acsaelm.3c01727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/01/2024] [Accepted: 02/01/2024] [Indexed: 03/05/2024]
Abstract
Resistive switching devices based on the Au/Ti/TiO2/Au stack were developed. In addition to standard electrical characterization by means of I-V curves, scanning thermal microscopy was employed to localize the hot spots on the top device surface (linked to conductive nanofilaments, CNFs) and perform in-operando tracking of temperature in such spots. In this way, electrical and thermal responses can be simultaneously recorded and related to each other. In a complementary way, a model for device simulation (based on COMSOL Multiphysics) was implemented in order to link the measured temperature to simulated device temperature maps. The data obtained were employed to calculate the thermal resistance to be used in compact models, such as the Stanford model, for circuit simulation. The thermal resistance extraction technique presented in this work is based on electrical and thermal measurements instead of being indirectly supported by a single fitting of the electrical response (using just I-V curves), as usual. Besides, the set and reset voltages were calculated from the complete I-V curve resistive switching series through different automatic numerical methods to assess the device variability. The series resistance was also obtained from experimental measurements, whose value is also incorporated into a compact model enhanced version.
Collapse
Affiliation(s)
- Juan B. Roldán
- Departamento
de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias. Avenida Fuentenueva s/n, 18071 Granada, Spain
| | - Antonio Cantudo
- Departamento
de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias. Avenida Fuentenueva s/n, 18071 Granada, Spain
| | - David Maldonado
- Departamento
de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias. Avenida Fuentenueva s/n, 18071 Granada, Spain
- IHP-Leibniz-Institut
für innovative Mikroelektronik, 15236 Frankfurt (Oder), Germany
| | - Cristina Aguilera-Pedregosa
- Departamento
de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias. Avenida Fuentenueva s/n, 18071 Granada, Spain
| | - Enrique Moreno
- CEMDATIC—E.T.S.I
Telecomunicación, Universidad Politécnica
de Madrid (UPM), 28040 Madrid, Spain
| | - Timm Swoboda
- Department
of Thermal and Fluid Engineering, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, The Netherlands
| | - Francisco Jiménez-Molinos
- Departamento
de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias. Avenida Fuentenueva s/n, 18071 Granada, Spain
| | - Yue Yuan
- Materials
Science and Engineering Program, Physical Sciences and Engineering
Division, King Abdullah University of Science
and Technology (KAUST), Thuwal 23955-6900, Saudi
Arabia
| | - Kaichen Zhu
- MIND, Department
of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain
| | - Mario Lanza
- Materials
Science and Engineering Program, Physical Sciences and Engineering
Division, King Abdullah University of Science
and Technology (KAUST), Thuwal 23955-6900, Saudi
Arabia
| | - Miguel Muñoz Rojo
- Department
of Thermal and Fluid Engineering, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, The Netherlands
- 2D
Foundry, Instituto de Ciencia de Materiales
de Madrid (ICMM), CSIC, Madrid 28049, Spain
| |
Collapse
|
5
|
Ghenzi N, Park TW, Kim SS, Kim HJ, Jang YH, Woo KS, Hwang CS. Heterogeneous reservoir computing in second-order Ta 2O 5/HfO 2 memristors. NANOSCALE HORIZONS 2024; 9:427-437. [PMID: 38086679 DOI: 10.1039/d3nh00493g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Multiple switching modes in a Ta2O5/HfO2 memristor are studied experimentally and numerically through a reservoir computing (RC) simulation to reveal the importance of nonlinearity and heterogeneity in the RC framework. Unlike most studies, where homogeneous reservoirs are used, heterogeneity is introduced by combining different behaviors of the memristor units. The chosen memristor for the reservoir units is based on a Ta2O5/HfO2 bilayer, in which the conductances of the Ta2O5 and HfO2 layers are controlled by the oxygen vacancies and deep/shallow traps, respectively, providing both volatile and non-volatile resistive switching modes. These several control parameters make the second-order Ta2O5/HfO2 memristor system present different behaviors in agreement with its history-dependent conductance and allow the fine-tuning of the behavior of each reservoir unit. The heterogeneity in the reservoir units improves the pattern recognition performance in the heterogeneous memristor RC system with a similar physical structure.
Collapse
Affiliation(s)
- Nestor Ghenzi
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea.
- Universidad de Avellaneda UNDAV and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
| | - Tae Won Park
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea.
| | - Seung Soo Kim
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea.
| | - Hae Jin Kim
- Department of Materials Science and Engineering, Myongji University, Yongin 17058, Korea
| | - Yoon Ho Jang
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea.
| | - Kyung Seok Woo
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea.
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea.
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Griffin K, Redmond G. Volatile Memristive Devices with Analog Resistance Switching Based on Self-Assembled Squaraine Microtubes as Synaptic Emulators. ACS APPLIED MATERIALS & INTERFACES 2024; 16:2539-2553. [PMID: 38174356 PMCID: PMC10797587 DOI: 10.1021/acsami.3c13735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/14/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024]
Abstract
In this work, the discovery of volatile memristive devices that exhibit analog resistive switching (RS) and synaptic emulation based on squaraine materials is presented. Specifically, organic microtubes (MTs) based on 2,4-bis[(4-(N,N-diisobutyl)-2-6-hydroxyphenyl]squaraine (SQ) are prepared by evaporation-induced self-assembly (EISA). The MTs are ca. 2 μm in diameter (aspect ratio: 10-130). While powder X-ray diffraction data for MTs identify monoclinic and orthorhombic polymorphs, optical data report the monoclinic phase with energetic disorder. By favorable energetic alignment of the Au work function with the SQ HOMO energy, unipolar (hole-only) symmetric metal-insulator-metal devices are formed by EISA of MT meshes on interdigitated electrodes. The DC I-V characteristics acquired exhibit pinched hysteretic I-V loops, indicative of memristive behavior. Analysis indicates Ohmic transport at low bias with carrier extraction by thermionic emission. At high bias, space-charge-limited conduction in the presence of traps distributed in energy, enhanced by a Poole-Frenkel effect and with carrier extraction by Fowler-Nordheim tunneling, is observed. These data indicate purely electronic conduction. I-V hysteresis attenuates at smaller voltage windows, suggesting that carrier trapping/detrapping underpins the hysteresis. By applying triangular voltage waveforms, device conductance gradually increases sweep-on-sweep, with wait-time-erase or voltage-erase options. Using square waveforms, repeated erase-write-read of multiple distinct conductance states is achieved. Such analog RS behavior is consistent with trap filling/emptying effects. By waveform design, volatile conductance states may also be written so that successive conductance states exhibit identical current levels, indicating forgetting of previously written states and mimicking the forgetting curve. Finally, advanced synaptic functions, i.e., excitatory postsynaptic current, paired-pulse facilitation, pulse-dependent plasticity, and a transition from short- to long-term memory driven by post-tetanic potentiation, are demonstrated.
Collapse
Affiliation(s)
- Karl Griffin
- School of Chemistry, University College Dublin, Belfield, Dublin 4, Ireland
| | - Gareth Redmond
- School of Chemistry, University College Dublin, Belfield, Dublin 4, Ireland
| |
Collapse
|
8
|
Obaidulla SM, Supina A, Kamal S, Khan Y, Kralj M. van der Waals 2D transition metal dichalcogenide/organic hybridized heterostructures: recent breakthroughs and emerging prospects of the device. NANOSCALE HORIZONS 2023; 9:44-92. [PMID: 37902087 DOI: 10.1039/d3nh00310h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
The near-atomic thickness and organic molecular systems, including organic semiconductors and polymer-enabled hybrid heterostructures, of two-dimensional transition metal dichalcogenides (2D-TMDs) can modulate their optoelectronic and transport properties outstandingly. In this review, the current understanding and mechanism of the most recent and significant breakthrough of novel interlayer exciton emission and its modulation by harnessing the band energy alignment between TMDs and organic semiconductors in a TMD/organic (TMDO) hybrid heterostructure are demonstrated. The review encompasses up-to-date device demonstrations, including field-effect transistors, detectors, phototransistors, and photo-switchable superlattices. An exploration of distinct traits in 2D-TMDs and organic semiconductors delves into the applications of TMDO hybrid heterostructures. This review provides insights into the synthesis of 2D-TMDs and organic layers, covering fabrication techniques and challenges. Band bending and charge transfer via band energy alignment are explored from both structural and molecular orbital perspectives. The progress in emission modulation, including charge transfer, energy transfer, doping, defect healing, and phase engineering, is presented. The recent advancements in 2D-TMDO-based optoelectronic synaptic devices, including various 2D-TMDs and organic materials for neuromorphic applications are discussed. The section assesses their compatibility for synaptic devices, revisits the operating principles, and highlights the recent device demonstrations. Existing challenges and potential solutions are discussed. Finally, the review concludes by outlining the current challenges that span from synthesis intricacies to device applications, and by offering an outlook on the evolving field of emerging TMDO heterostructures.
Collapse
Affiliation(s)
- Sk Md Obaidulla
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
- Department of Condensed Matter and Materials Physics, S. N. Bose National Centre for Basic Sciences, Sector III, Block JD, Salt Lake, Kolkata 700106, India
| | - Antonio Supina
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
- Chair of Physics, Montanuniversität Leoben, Franz Josef Strasse 18, 8700 Leoben, Austria
| | - Sherif Kamal
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
| | - Yahya Khan
- Department of Physics, Karakoram International university (KIU), Gilgit 15100, Pakistan
| | - Marko Kralj
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
| |
Collapse
|
9
|
Abstract
Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.
Collapse
Affiliation(s)
- Ke He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Cong Wang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yongli He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Jiangtao Su
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Xiaodong Chen
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore
| |
Collapse
|
10
|
Gao H, Zou M, Zhong C, Zhuang J, Lin J, Lu Z, Jiang Z, Lu Y, Chen Z, Guo W. Advances in pixel driving technology for micro-LED displays. NANOSCALE 2023; 15:17232-17248. [PMID: 37856207 DOI: 10.1039/d3nr01649h] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
Micro-LED displays have been recognized as the next-generation display technology. This review focuses on the pixel-driving technology of micro-LED displays. The performance of pixel driving on micro-LED displays is discussed in terms of brightness uniformity, driving speed, grayscale, and frame rate under various driving architectures. Since the memristors possess characteristics similar to those of biological synaptic neurons due to the ion migration mechanism, the neural network approach which combines the memristor arrays with the pixel driving circuit of micro-LEDs could promote the development of smart and efficient displays.
Collapse
Affiliation(s)
- Han Gao
- National Innovation Platform for the Fusion of Industry and Education in Integrated Circuits, Department of Electronic Science, School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China.
- China, and also with the Shenzhen Research Institute of Xiamen University, Shenzhen 518057, China
| | - Mingjie Zou
- National Innovation Platform for the Fusion of Industry and Education in Integrated Circuits, Department of Electronic Science, School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China.
- China, and also with the Shenzhen Research Institute of Xiamen University, Shenzhen 518057, China
| | - Chenming Zhong
- National Innovation Platform for the Fusion of Industry and Education in Integrated Circuits, Department of Electronic Science, School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China.
- China, and also with the Shenzhen Research Institute of Xiamen University, Shenzhen 518057, China
| | | | - Junjie Lin
- AUO (Xiamen) Co. Ltd, Xiamen 361102, China.
| | - Zhian Lu
- AUO (Xiamen) Co. Ltd, Xiamen 361102, China.
| | | | - Yijun Lu
- National Innovation Platform for the Fusion of Industry and Education in Integrated Circuits, Department of Electronic Science, School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China.
- China, and also with the Shenzhen Research Institute of Xiamen University, Shenzhen 518057, China
| | - Zhong Chen
- National Innovation Platform for the Fusion of Industry and Education in Integrated Circuits, Department of Electronic Science, School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China.
- China, and also with the Shenzhen Research Institute of Xiamen University, Shenzhen 518057, China
| | - Weijie Guo
- National Innovation Platform for the Fusion of Industry and Education in Integrated Circuits, Department of Electronic Science, School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China.
- China, and also with the Shenzhen Research Institute of Xiamen University, Shenzhen 518057, China
| |
Collapse
|
11
|
Yin K, Li J, Xiong Y, Zhu M, Tan Z, Jin Z. Simulating Synaptic Behaviors through Frequency Modulation in a Capacitor-Memristor Circuit. MICROMACHINES 2023; 14:2014. [PMID: 38004871 PMCID: PMC10673497 DOI: 10.3390/mi14112014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/20/2023] [Accepted: 10/27/2023] [Indexed: 11/26/2023]
Abstract
Memristors, known for their adjustable and non-volatile resistance, offer a promising avenue for emulating synapses. However, achieving pulse frequency-dependent synaptic plasticity in memristors or memristive systems necessitates further exploration. In this study, we present a novel approach to modulate the conductance of a memristor in a capacitor-memristor circuit by finely tuning the frequency of input pulses. Our experimental results demonstrate that these phenomena align with the long-term depression (LTD) and long-term potentiation (LTP) observed in synapses, which are induced by the frequency of action potentials. Additionally, we successfully implement a Hebbian-like learning mechanism in a simple circuit that connects a pair of memristors to a capacitor, resulting in observed associative memory formation and forgetting processes. Our findings highlight the potential of capacitor-memristor circuits in faithfully replicating the frequency-dependent behavior of synapses, thereby offering a valuable contribution to the development of brain-inspired neural networks.
Collapse
Affiliation(s)
- Kuibo Yin
- SEU-FEI Nano-Pico Center, Key Laboratory of MEMS of Ministry of Education, Southeast University, Nanjing 210096, China
| | | | | | | | | | | |
Collapse
|
12
|
Park SO, Park T, Jeong H, Hong S, Seo S, Kwon Y, Lee J, Choi S. Linear conductance update improvement of CMOS-compatible second-order memristors for fast and energy-efficient training of a neural network using a memristor crossbar array. NANOSCALE HORIZONS 2023; 8:1366-1376. [PMID: 37403772 DOI: 10.1039/d3nh00121k] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/06/2023]
Abstract
Memristors are two-terminal memory devices that can change the conductance state and store analog values. Thanks to their simple structure, suitability for high-density integration, and non-volatile characteristics, memristors have been intensively studied as synapses in artificial neural network systems. Memristive synapses in neural networks have theoretically better energy efficiency compared with conventional von Neumann computing processors. However, memristor crossbar array-based neural networks usually suffer from low accuracy because of the non-ideal factors of memristors such as non-linearity and asymmetry, which prevent weights from being programmed to their targeted values. In this article, the improvement in linearity and symmetry of pulse update of a fully CMOS-compatible HfO2-based memristor is discussed, by using a second-order memristor effect with a heating pulse and a voltage divider composed of a series resistor and two diodes. We also demonstrate that the improved device characteristics enable energy-efficient and fast training of a memristor crossbar array-based neural network with high accuracy through a realistic model-based simulation. By improving the memristor device's linearity and symmetry, our results open up the possibility of a trainable memristor crossbar array-based neural network system that possesses great energy efficiency, high area efficiency, and high accuracy at the same time.
Collapse
Affiliation(s)
- See-On Park
- The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Taehoon Park
- The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Hakcheon Jeong
- The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Seokman Hong
- The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Seokho Seo
- The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Yunah Kwon
- Nano Convergence Technology Division, National Nanofab Center (NNFC), 291, Daehak-ro, Daejeon 34141, Republic of Korea.
| | - Jongwon Lee
- Nano Convergence Technology Division, National Nanofab Center (NNFC), 291, Daehak-ro, Daejeon 34141, Republic of Korea.
| | - Shinhyun Choi
- The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| |
Collapse
|
13
|
Chen S, Zhang T, Tappertzhofen S, Yang Y, Valov I. Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301924. [PMID: 37199224 DOI: 10.1002/adma.202301924] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/22/2023] [Indexed: 05/19/2023]
Abstract
Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.
Collapse
Affiliation(s)
- Shaochuan Chen
- Institute of Materials in Electrical Engineering 2 (IWE2), RWTH Aachen University, Sommerfeldstraße 24, 52074, Aachen, Germany
| | - Teng Zhang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Stefan Tappertzhofen
- Chair for Micro- and Nanoelectronics, Department of Electrical Engineering and Information Technology, TU Dortmund University, Martin-Schmeisser-Weg 4-6, D-44227, Dortmund, Germany
| | - Yuchao Yang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing, 100871, China
- School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China
- Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, 102206, China
| | - Ilia Valov
- Peter Grünberg Institute (PGI-7), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
- Institute of Electrochemistry and Energy Systems "Acad. E. Budewski", Bulgarian Academy of Sciences, Acad. G. Bonchev 10, 1113, Sofia, Bulgaria
| |
Collapse
|
14
|
Kim H, Seo J, Cho S, Jeon S, Woo J, Lee D. Three-dimensional vertical structural electrochemical random access memory for high-density integrated synapse device. Sci Rep 2023; 13:14325. [PMID: 37652919 PMCID: PMC10471571 DOI: 10.1038/s41598-023-41202-5] [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: 06/10/2023] [Accepted: 08/23/2023] [Indexed: 09/02/2023] Open
Abstract
Three-terminal (3T) structured electrochemical random access memory (ECRAM) has been proposed as a synaptic device based on improved synaptic characteristics. However, the proposed 3T ECRAM has a larger area requirement than 2T synaptic devices; thereby limiting integration density. To overcome this limitation, this study presents the development of a high-density vertical structure for the 3T ECRAM. In addition, complementary metal-oxide semiconductor (CMOS)-compatible materials and 8-inch wafer-based CMOS fabrication processes were utilized to verify the feasibility of mass production. The achievements of this work demonstrate the potential for high-density integration and mass production of 3T ECRAM devices.
Collapse
Affiliation(s)
- Hyejin Kim
- Department of Electronic Materials Engineering, Kwangwoon University, Seoul, 01897, Republic of Korea
| | - Jongseon Seo
- Department of Electronic Materials Engineering, Kwangwoon University, Seoul, 01897, Republic of Korea
| | - Seojin Cho
- Department of Electronic Materials Engineering, Kwangwoon University, Seoul, 01897, Republic of Korea
| | - Seonuk Jeon
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea
| | - Jiyong Woo
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea
| | - Daeseok Lee
- Department of Electronic Materials Engineering, Kwangwoon University, Seoul, 01897, Republic of Korea.
| |
Collapse
|
15
|
Lee DH, Park H, Cho WJ. Implementation of Highly Stable Memristive Characteristics in an Organic-Inorganic Hybrid Resistive Switching Layer of Chitosan-Titanium Oxide with Microwave-Assisted Oxidation. Molecules 2023; 28:5174. [PMID: 37446836 DOI: 10.3390/molecules28135174] [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: 05/31/2023] [Revised: 06/23/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
This study proposes a high-performance organic-inorganic hybrid memristor for the development of neuromorphic devices in the memristor-based artificial synapse. The memristor consists of a solid polymer electrolyte (SPE) chitosan layer and a titanium oxide (TiOx) layer grown with a low-thermal-budget, microwave-assisted oxidation. The fabricated Ti/SPE-chitosan/TiOx/Pt-structured memristor exhibited steady bipolar resistive switching (BRS) characteristics and demonstrated excellent endurance in 100-cycle repetition tests. Compared to SPE-chitosan memristors without a TiOx layer, the proposed organic-inorganic hybrid memristor demonstrated a higher dynamic range and a higher response to pre-synaptic stimuli such as short-term plasticity via paired-pulse facilitation. The effect of adding the TiOx layer on the BRS properties was examined, and the results showed that the TiOx layer improved the chemical and electrical superiority of the proposed memristor synaptic device. The proposed SPE-chitosan organic-inorganic hybrid memristor also exhibited a stable spike-timing-dependent plasticity, which closely mimics long-term plasticity. The potentiation and depression behaviors that modulate synaptic weights operated stably via repeated spike cycle tests. Therefore, the proposed SPE-chitosan organic-inorganic hybrid memristor is a promising candidate for the development of neuromorphic devices in memristor-based artificial synapses owing to its excellent stability, high dynamic range, and superior response to pre-synaptic stimuli.
Collapse
Affiliation(s)
- Dong-Hee Lee
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Hamin Park
- Department of Electronic Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Won-Ju Cho
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| |
Collapse
|
16
|
He L, Yang Z, Wang Z, Leydecker T, Orgiu E. Organic multilevel (opto)electronic memories towards neuromorphic applications. NANOSCALE 2023. [PMID: 37378458 DOI: 10.1039/d3nr01311a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
In the past decades, neuromorphic computing has attracted the interest of the scientific community due to its potential to circumvent the von Neumann bottleneck. Organic materials, owing to their fine tunablility and their ability to be used in multilevel memories, represent a promising class of materials to fabricate neuromorphic devices with the key requirement of operation with synaptic weight. In this review, recent studies of organic multilevel memory are presented. The operating principles and the latest achievements obtained with devices exploiting the main approaches to reach multilevel operation are discussed, with emphasis on organic devices using floating gates, ferroelectric materials, polymer electrets and photochromic molecules. The latest results obtained using organic multilevel memories for neuromorphic circuits are explored and the major advantages and drawbacks of the use of organic materials for neuromorphic applications are discussed.
Collapse
Affiliation(s)
- Lin He
- Institute of Fundamental and Frontier Sciences (IFFS), University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Zuchong Yang
- Institut national de la recherche scientifique (INRS), Centre Énergie Matériaux Télécommunications, 1650 Boul. Lionel Boulet, Varennes J3X 1S2, Canada.
| | - Zhiming Wang
- Institute of Fundamental and Frontier Sciences (IFFS), University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Tim Leydecker
- Institute of Fundamental and Frontier Sciences (IFFS), University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Emanuele Orgiu
- Institut national de la recherche scientifique (INRS), Centre Énergie Matériaux Télécommunications, 1650 Boul. Lionel Boulet, Varennes J3X 1S2, Canada.
| |
Collapse
|
17
|
Park W, Kim G, In JH, Rhee H, Song H, Park J, Martinez A, Kim KM. High Amplitude Spike Generator in Au Nanodot-Incorporated NbO x Mott Memristor. NANO LETTERS 2023; 23:5399-5407. [PMID: 36930534 DOI: 10.1021/acs.nanolett.2c04599] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
NbOx-based Mott memristors exhibit fast threshold switching behaviors, making them suitable for spike generators in neuromorphic computing and stochastic clock generators in security devices. In these applications, a high output spike amplitude is necessary for threshold level control and accurate signal detection. Here, we propose a materialwise solution to obtain the high amplitude spikes by inserting Au nanodots into the NbOx device. The Au nanodots enable increasing the threshold voltage by modulating the oxygen contents at the electrode-oxide interface, providing a higher ON current compared to nanodot-free NbOx devices. Also, the reduction of the local switching region volume decreases the thermal capacitance of the system, allowing the maximum spike amplitude generation. Consequently, the Au nanodot incorporation increases the spike amplitude of the NbOx device by 6 times, without any additional external circuit elements. The results are systematically supported by both a numerical model and a finite-element-method-based multiphysics model.
Collapse
Affiliation(s)
- Woojoon Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Gwangmin Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Jae Hyun In
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Hakseung Rhee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Hanchan Song
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Juseong Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Alba Martinez
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Kyung Min Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| |
Collapse
|
18
|
Wang L, Zuo Z, Wen D. Realization of Artificial Nerve Synapses Based on Biological Threshold Resistive Random Access Memory. Adv Biol (Weinh) 2023; 7:e2200298. [PMID: 36650948 DOI: 10.1002/adbi.202200298] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/09/2022] [Indexed: 01/19/2023]
Abstract
A one-selector one resistor (1S1R) array formed of a selector and resistive random access memory (RRAM) is an important way to achieve high-density storage and neuromorphic computing. However, the low durability and poor consistency of the selector limit its practical application. The fabrication of a selector based on egg albumen (EA) is reported in this paper. The device exhibits excellent bidirectional threshold switching characteristics, including a low leakage current (10-7 A), a high ON/OFF current ratio (106 ), and good endurance (>700 days). It is used as a selector to form a 1S1R unit in combination with an EA-based RRAM to effectively solve the leakage current in a crossbar array. A feasible solution is provided for the realization of a protein-based 1S1R array to achieve high-density storage. The 1S1R unit shows characteristics similar to those of synapses in the human brain under impulse excitation and has great potential in simulating the human brain for neuromorphic calculations.).
Collapse
Affiliation(s)
- Lu Wang
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, P. R. China
| | - Ze Zuo
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, P. R. China
| | - Dianzhong Wen
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, P. R. China
| |
Collapse
|
19
|
Han J, Shan X, Lin Y, Tao Y, Zhao X, Wang Z, Xu H, Liu Y. Multi-Wavelength-Recognizable Memristive Devices via Surface Plasmon Resonance Effect for Color Visual System. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2207928. [PMID: 36890789 DOI: 10.1002/smll.202207928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/04/2023] [Indexed: 06/08/2023]
Abstract
Photoelectric memristor has attracted many attentions thanks to their promising potential in optical communication chips and artificial vision systems. However, the implementation of an artificial visual system based on memristive devices remains a considerable challenge because most photoelectric memristors cannot recognize color. Herein, multi-wavelength recognizable memristive devices based on silver(Ag) nanoparticles (NPs) and porous silicon oxide (SiOx ) nanocomposites are presented. Rely on the effects of localized surface plasmon resonance (LSPR) and optical excitation of Ag NPs in SiOx , the set voltage of the device can be gradually reduced. Moreover, the current overshoot problem is alleviated to suppress conducting filament overgrowth after visible light irradiation with different wavelengths, resulting in diverse low resistance states (LRS). Taking advantage of the characteristics of controlled switching voltage and LRS resistance distribution, color image recognition is finally realized in the present work. X-ray photoelectron spectroscopy (XPS) and conductive atomic force microscopy (C-AFM) show that the light irradiation plays an important role on resistive switching (RS) process: the photo-assisted Ag ionization leads to a significant reduction of set voltage and overshoot current. This work provides an effective method toward the development of multi-wavelength-recognizable memristive devices for future artificial color vision system.
Collapse
Affiliation(s)
- Jiaqi Han
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Xuanyu Shan
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Ya Lin
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Ye Tao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Xiaoning Zhao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Zhongqiang Wang
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Haiyang Xu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| | - Yichun Liu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, China
| |
Collapse
|
20
|
Cao Z, Sun B, Zhou G, Mao S, Zhu S, Zhang J, Ke C, Zhao Y, Shao J. Memristor-based neural networks: a bridge from device to artificial intelligence. NANOSCALE HORIZONS 2023; 8:716-745. [PMID: 36946082 DOI: 10.1039/d2nh00536k] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.
Collapse
Affiliation(s)
- Zelin Cao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
- Shaanxi International Joint Research Center for Applied Technology of Controllable Neutron Source, School of Science, Xijing University, Xi'an 710123, China
| | - Bai Sun
- Frontier Institute of Science and Technology (FIST), 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
| | - Shuangsuo Mao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Shouhui Zhu
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Jie Zhang
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Chuan Ke
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Yong Zhao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Jinyou Shao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
| |
Collapse
|
21
|
Kim H, Kim M, Lee A, Park HL, Jang J, Bae JH, Kang IM, Kim ES, Lee SH. Organic Memristor-Based Flexible Neural Networks with Bio-Realistic Synaptic Plasticity for Complex Combinatorial Optimization. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2300659. [PMID: 37189211 DOI: 10.1002/advs.202300659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/19/2023] [Indexed: 05/17/2023]
Abstract
Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Several studies have been conducted on flexible neural networks for practical applications; however, developing systems with complete synaptic plasticity for combinatorial optimization remains challenging. In this study, the metal-ion injection density is explored as a diffusive parameter of the conductive filament in organic memristors. Additionally, a flexible artificial synapse with bio-realistic synaptic plasticity is developed using organic memristors that have systematically engineered metal-ion injections, for the first time. In the proposed artificial synapse, short-term plasticity (STP), long-term plasticity, and homeostatic plasticity are independently achieved and are analogous to their biological counterparts. The time windows of the STP and homeostatic plasticity are controlled by the ion-injection density and electric-signal conditions, respectively. Moreover, stable capabilities for complex combinatorial optimization in the developed synapse arrays are demonstrated under spike-dependent operations. This effective concept for realizing flexible neuromorphic systems for complex combinatorial optimization is an essential building block for achieving a new paradigm of wearable smart electronics associated with artificial intelligent systems.
Collapse
Affiliation(s)
- Hyeongwook Kim
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - Miseong Kim
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - Aejin Lee
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - Hea-Lim Park
- Department of Materials Science and Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea
| | - Jaewon Jang
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - Jin-Hyuk Bae
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - In Man Kang
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - Eun-Sol Kim
- Department of Computer Science, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Sin-Hyung Lee
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| |
Collapse
|
22
|
Lee HC, Kim J, Kim HR, Kim KH, Park KJ, So JP, Lee JM, Hwang MS, Park HG. Nanograin network memory with reconfigurable percolation paths for synaptic interactions. LIGHT, SCIENCE & APPLICATIONS 2023; 12:118. [PMID: 37188669 DOI: 10.1038/s41377-023-01168-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 04/15/2023] [Accepted: 04/23/2023] [Indexed: 05/17/2023]
Abstract
The development of memory devices with functions that simultaneously process and store data is required for efficient computation. To achieve this, artificial synaptic devices have been proposed because they can construct hybrid networks with biological neurons and perform neuromorphic computation. However, irreversible aging of these electrical devices causes unavoidable performance degradation. Although several photonic approaches to controlling currents have been suggested, suppression of current levels and switching of analog conductance in a simple photonic manner remain challenging. Here, we demonstrated a nanograin network memory using reconfigurable percolation paths in a single Si nanowire with solid core/porous shell and pure solid core segments. The electrical and photonic control of current percolation paths enabled the analog and reversible adjustment of the persistent current level, exhibiting memory behavior and current suppression in this single nanowire device. In addition, the synaptic behaviors of memory and erasure were demonstrated through potentiation and habituation processes. Photonic habituation was achieved using laser illumination on the porous nanowire shell, with a linear decrease in the postsynaptic current. Furthermore, synaptic elimination was emulated using two adjacent devices interconnected on a single nanowire. Therefore, electrical and photonic reconfiguration of the conductive paths in Si nanograin networks will pave the way for next-generation nanodevice technologies.
Collapse
Affiliation(s)
- Hoo-Cheol Lee
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Jungkil Kim
- Department of Physics, Jeju National University, Jeju, 63243, Republic of Korea.
| | - Ha-Reem Kim
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Kyoung-Ho Kim
- Department of Physics, Chungbuk National University, Cheongju, 28644, Republic of Korea
| | - Kyung-Jun Park
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Jae-Pil So
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Jung Min Lee
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Min-Soo Hwang
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Hong-Gyu Park
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea.
| |
Collapse
|
23
|
Kumar D, Joharji L, Li H, Rezk A, Nayfeh A, El-Atab N. Artificial visual perception neural system using a solution-processable MoS 2-based in-memory light sensor. LIGHT, SCIENCE & APPLICATIONS 2023; 12:109. [PMID: 37147334 PMCID: PMC10162957 DOI: 10.1038/s41377-023-01166-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/08/2023] [Accepted: 04/19/2023] [Indexed: 05/07/2023]
Abstract
Optoelectronic devices are advantageous in in-memory light sensing for visual information processing, recognition, and storage in an energy-efficient manner. Recently, in-memory light sensors have been proposed to improve the energy, area, and time efficiencies of neuromorphic computing systems. This study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor (MOS) charge-trapping memory structure-the basic structure for charge-coupled devices (CCD)-and showing its suitability for in-memory light sensing and artificial visual perception. The memory window of the device increased from 2.8 V to more than 6 V when the device was irradiated with optical lights of different wavelengths during the program operation. Furthermore, the charge retention capability of the device at a high temperature (100 °C) was enhanced from 36 to 64% when exposed to a light wavelength of 400 nm. The larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al2O3/MoS2 interface and in the MoS2 layer. A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the device. The array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91% accuracy. This study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception, adaptive parallel processing networks for in-memory light sensing, and smart CCD cameras with artificial visual perception capabilities.
Collapse
Affiliation(s)
- Dayanand Kumar
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering Program, Computer Electrical Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Kingdom of Saudi Arabia
| | - Lana Joharji
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering Program, Computer Electrical Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Kingdom of Saudi Arabia
| | - Hanrui Li
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering Program, Computer Electrical Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Kingdom of Saudi Arabia
| | - Ayman Rezk
- Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, 127788, United Arab Emirates
| | - Ammar Nayfeh
- Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, 127788, United Arab Emirates
| | - Nazek El-Atab
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering Program, Computer Electrical Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Kingdom of Saudi Arabia.
| |
Collapse
|
24
|
Wang X, Yang H, Li E, Cao C, Zheng W, Chen H, Li W. Stretchable Transistor-Structured Artificial Synapses for Neuromorphic Electronics. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2205395. [PMID: 36748849 DOI: 10.1002/smll.202205395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 01/12/2023] [Indexed: 05/04/2023]
Abstract
Stretchable synaptic transistors, a core technology in neuromorphic electronics, have functions and structures similar to biological synapses and can concurrently transmit signals and learn. Stretchable synaptic transistors are usually soft and stretchy and can accommodate various mechanical deformations, which presents significant prospects in soft machines, electronic skin, human-brain interfaces, and wearable electronics. Considerable efforts have been devoted to developing stretchable synaptic transistors to implement electronic device neuromorphic functions, and remarkable advances have been achieved. Here, this review introduces the basic concept of artificial synaptic transistors and summarizes the recent progress in device structures, functional-layer materials, and fabrication processes. Classical stretchable synaptic transistors, including electric double-layer synaptic transistors, electrochemical synaptic transistors, and optoelectronic synaptic transistors, as well as the applications of stretchable synaptic transistors in light-sensory systems, tactile-sensory systems, and multisensory artificial-nerves systems, are discussed. Finally, the current challenges and potential directions of stretchable synaptic transistors are analyzed. This review presents a detailed introduction to the recent progress in stretchable synaptic transistors from basic concept to applications, providing a reference for the development of stretchable synaptic transistors in the future.
Collapse
Affiliation(s)
- Xiumei Wang
- School of Science, Anhui Agricultural University, Hefei, 230036, China
| | - Huihuang Yang
- School of Science, Anhui Agricultural University, Hefei, 230036, China
| | - Enlong Li
- Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics, Department of Materials Science, Fudan University, Shanghai, 200433, China
| | - Chunbin Cao
- School of Science, Anhui Agricultural University, Hefei, 230036, China
| | - Wen Zheng
- School of Science, Anhui Agricultural University, Hefei, 230036, China
- School of Information & Computer, Anhui Agricultural University, Hefei, 230036, China
| | - Huipeng Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Wenwu Li
- Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics, Department of Materials Science, Fudan University, Shanghai, 200433, China
- National Key Laboratory of Integrated Circuit Chips and Systems, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, China
| |
Collapse
|
25
|
Zeng T, Wang Z, Lin Y, Cheng Y, Shan X, Tao Y, Zhao X, Xu H, Liu Y. Doppler Frequency-Shift Information Processing in WO x -Based Memristive Synapse for Auditory Motion Perception. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300030. [PMID: 36862024 PMCID: PMC10161103 DOI: 10.1002/advs.202300030] [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: 01/03/2023] [Revised: 02/10/2023] [Indexed: 05/06/2023]
Abstract
Auditory motion perception is one crucial capability to decode and discriminate the spatiotemporal information for neuromorphic auditory systems. Doppler frequency-shift feature and interaural time difference (ITD) are two fundamental cues of auditory information processing. In this work, the functions of azimuth detection and velocity detection, as the typical auditory motion perception, are demonstrated in a WOx -based memristive synapse. The WOx memristor presents both the volatile mode (M1) and semi-nonvolatile mode (M2), which are capable of implementing the high-pass filtering and processing the spike trains with a relative timing and frequency shift. In particular, the Doppler frequency-shift information processing for velocity detection is emulated in the WOx memristor based auditory system for the first time, which relies on a scheme of triplet spike-timing-dependent-plasticity in the memristor. These results provide new opportunities for the mimicry of auditory motion perception and enable the auditory sensory system to be applied in future neuromorphic sensing.
Collapse
Affiliation(s)
- Tao Zeng
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Zhongqiang Wang
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Ya Lin
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - YanKun Cheng
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Xuanyu Shan
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Ye Tao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Xiaoning Zhao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Haiyang Xu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Yichun Liu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| |
Collapse
|
26
|
Dong Z, Hua Q, Xi J, Shi Y, Huang T, Dai X, Niu J, Wang B, Wang ZL, Hu W. Ultrafast and Low-Power 2D Bi 2O 2Se Memristors for Neuromorphic Computing Applications. NANO LETTERS 2023; 23:3842-3850. [PMID: 37093653 DOI: 10.1021/acs.nanolett.3c00322] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Memristors that emulate synaptic plasticity are building blocks for opening a new era of energy-efficient neuromorphic computing architecture, which will overcome the limitation of the von Neumann bottleneck. Layered two-dimensional (2D) Bi2O2Se, as an emerging material for next-generation electronics, is of great significance in improving the efficiency and performance of memristive devices. Herein, high-quality Bi2O2Se nanosheets are grown by configuring mica substrates face-down on the Bi2O2Se powder. Then, bipolar Bi2O2Se memristors are fabricated with excellent performance including ultrafast switching speed (<5 ns) and low-power consumption (<3.02 pJ). Moreover, synaptic plasticity, such as long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are demonstrated in the Bi2O2Se memristor. Furthermore, MNIST recognition with simulated artificial neural networks (ANN) based on conductance modification could reach a high accuracy of 91%. Notably, the 2D Bi2O2Se enables the memristor to possess ultrafast and low-power attributes, showing great potential in neuromorphic computing applications.
Collapse
Affiliation(s)
- Zilong Dong
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qilin Hua
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Jianguo Xi
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
| | - Yuanhong Shi
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tianci Huang
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinhuan Dai
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianan Niu
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bingjun Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhong Lin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiguo Hu
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
27
|
Tsurumaki-Fukuchi A, Katase T, Ohta H, Arita M, Takahashi Y. Direct Imaging of Ion Migration in Amorphous Oxide Electronic Synapses with Intrinsic Analog Switching Characteristics. ACS APPLIED MATERIALS & INTERFACES 2023; 15:16842-16852. [PMID: 36952672 PMCID: PMC10080533 DOI: 10.1021/acsami.2c21568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/13/2023] [Indexed: 06/18/2023]
Abstract
Amorphous metal oxides with analog resistive switching functions (i.e., continuous controllability of the electrical resistance) are gaining emerging interest due to their neuromorphic functionalities promising for energy efficient electronics. The mechanisms are currently attributed to field-driven migration of the constituent ions, but the applications are being hindered by the limited understanding of the physical mechanisms due to the difficulty in analyzing the causal ion migration, which occurs on a nanometer or even atomic scale. Here, the direct electrical transport measurement of analog resistive switching and ångström scale imaging of the causal ion migration is demonstrated in amorphous TaOx (a-TaOx) by conductive atomic force microscopy. Atomically flat thin films of a-TaOx, which is a practical material for commercial resistive random access memory, are fabricated in this study, and the mechanisms of the three known types of analog resistive switching phenomena (current-dependent set, voltage-dependent reset, and time-dependent switching) are directly visualized on the surfaces. The observations indicate that highly analog type of resistive switching can be induced in a-TaOx by inducing the continuous redox reactions for 2.0 < x < 2.5, which are characteristic of a-TaOx. The measurements also demonstrate drastic control of the switching stochasticity, which is attributable to controlled segregation of a metastable a-TaO2 phase. The findings provide direct clues for tuning the analog resistive switching characteristics of amorphous metal oxides and developing new functions for future neuromorphic computing.
Collapse
Affiliation(s)
| | - Takayoshi Katase
- Laboratory
for Materials and Structures, Institute
of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
| | - Hiromichi Ohta
- Research
Institute for Electronic Science, Hokkaido
University, Sapporo 001-0020, Japan
| | - Masashi Arita
- Faculty
of Information Science and Technology, Hokkaido
University, Sapporo 060-0814, Japan
| | - Yasuo Takahashi
- Faculty
of Information Science and Technology, Hokkaido
University, Sapporo 060-0814, Japan
| |
Collapse
|
28
|
Liu YH, Wang JJ, Wang HZ, Liu S, Wu YC, Hu SG, Yu Q, Liu Z, Chen TP, Yin Y, Liu Y. Braille recognition by E-skin system based on binary memristive neural network. Sci Rep 2023; 13:5437. [PMID: 37012399 PMCID: PMC10070348 DOI: 10.1038/s41598-023-31934-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Braille system is widely used worldwide for communication by visually impaired people. However, there are still some visually impaired people who are unable to learn Braille system due to various factors, such as the age (too young or too old), brain damage, etc. A wearable and low-cost Braille recognition system may substantially help these people recognize Braille or assist them in Braille learning. In this work, we fabricated polydimethylsiloxane (PDMS)-based flexible pressure sensors to construct an electronic skin (E-skin) for the application of Braille recognition. The E-skin mimics human touch sensing function for collecting Braille information. Braille recognition is realized with a neural network based on memristors. We utilize a binary neural network algorithm with only two bias layers and three fully connected layers. Such neural network design remarkably reduces the calculation burden and, thus, the system cost. Experiments show that the system can achieve a recognition accuracy of up to 91.25%. This work demonstrates the possibility of realizing a wearable and low-cost Braille recognition system and a Braille learning-assistance system.
Collapse
Affiliation(s)
- Y H Liu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - J J Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
| | - H Z Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - S Liu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Y C Wu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - S G Hu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Q Yu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Z Liu
- School of Integrated Circuits, Guangdong University of Technology, Guangzhou, 510006, China
| | - T P Chen
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Y Yin
- Graduate School of Engineering, Gunma University, 1-5-1Tenjin, Kiryu, Gunma, 376-8515, Japan
| | - Y Liu
- Deepcreatic Technologies Ltd, Chengdu, 610000, Sichuan, People's Republic of China
| |
Collapse
|
29
|
Zhou W, Wen S, Liu Y, Liu L, Liu X, Chen L. Forgetting memristor based STDP learning circuit for neural networks. Neural Netw 2023; 158:293-304. [PMID: 36493532 DOI: 10.1016/j.neunet.2022.11.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 10/18/2022] [Accepted: 11/14/2022] [Indexed: 11/21/2022]
Abstract
The circuit implementation of STDP based on memristor is of great significance for the application of neural network. However, recent research shows that the research on the pure circuit implementation of forgetting memristor and STDP is still rare. This paper proposes a new STDP learning rule implementation circuit based on the forgetting memristor. This kind of forgetting memory resistance synapse makes the neural network have the function of time-division multiplexing, but the instability of short-term memory will affect the learning ability of the neural network. This paper analyzes and discusses the influence of synapses with long-term and short-term memory on the learning characteristics of neural network STDP, which lays a foundation for the construction of time-division multiplexing neural network with long-term and short-term memory synapses. Through this circuit, it is found that the volatile memristor has different behaviors to the stimulus signal in different initial states, and the resulting LTP phenomenon is more in line with the forgetting effect in biology. This circuit has multiple adjustable parameters, which can fit the STDP learning rules under different conditions. The application of neural network proves the availability of this circuit.
Collapse
Affiliation(s)
- Wenhao Zhou
- Electronic Information and Engineering, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, 400715, China.
| | - Shiping Wen
- Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
| | - Yi Liu
- Electronic Information and Engineering, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, 400715, China
| | - Lu Liu
- Electronic Information and Engineering, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, 400715, China
| | - Xin Liu
- Computer Vision and Pattern Recognition Laboratory, School of Engineering Science, Lappeenranta-Lahti University of Technology LUT, Finland.
| | - Ling Chen
- Electronic Information and Engineering, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, 400715, China; Computer Vision and Pattern Recognition Laboratory, School of Engineering Science, Lappeenranta-Lahti University of Technology LUT, Finland.
| |
Collapse
|
30
|
John RA, Milozzi A, Tsarev S, Brönnimann R, Boehme SC, Wu E, Shorubalko I, Kovalenko MV, Ielmini D. Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity. SCIENCE ADVANCES 2022; 8:eade0072. [PMID: 36563153 PMCID: PMC9788778 DOI: 10.1126/sciadv.ade0072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/04/2022] [Indexed: 06/17/2023]
Abstract
With increasing computing demands, serial processing in von Neumann architectures built with zeroth-order complexity digital circuits is saturating in computational capacity and power, entailing research into alternative paradigms. Brain-inspired systems built with memristors are attractive owing to their large parallelism, low energy consumption, and high error tolerance. However, most demonstrations have thus far only mimicked primitive lower-order biological complexities using devices with first-order dynamics. Memristors with higher-order complexities are predicted to solve problems that would otherwise require increasingly elaborate circuits, but no generic design rules exist. Here, we present second-order dynamics in halide perovskite memristive diodes (memdiodes) that enable Bienenstock-Cooper-Munro learning rules capturing both timing- and rate-based plasticity. A triplet spike timing-dependent plasticity scheme exploiting ion migration, back diffusion, and modulable Schottky barriers establishes general design rules for realizing higher-order memristors. This higher order enables complex binocular orientation selectivity in neural networks exploiting the intrinsic physics of the devices, without the need for complicated circuitry.
Collapse
Affiliation(s)
- Rohit Abraham John
- Department of Chemistry and Applied Biosciences, Institute of Inorganic Chemistry, ETH Zürich, Zürich CH-8093, Switzerland
- Empa-Swiss Federal Laboratories for Materials Science and Technology, Dübendorf CH-8600, Switzerland
| | - Alessandro Milozzi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza L. da Vinci 32, Milano 20133, Italy
| | - Sergey Tsarev
- Department of Chemistry and Applied Biosciences, Institute of Inorganic Chemistry, ETH Zürich, Zürich CH-8093, Switzerland
- Empa-Swiss Federal Laboratories for Materials Science and Technology, Dübendorf CH-8600, Switzerland
| | - Rolf Brönnimann
- Empa-Swiss Federal Laboratories for Materials Science and Technology, Dübendorf CH-8600, Switzerland
| | - Simon C. Boehme
- Department of Chemistry and Applied Biosciences, Institute of Inorganic Chemistry, ETH Zürich, Zürich CH-8093, Switzerland
- Empa-Swiss Federal Laboratories for Materials Science and Technology, Dübendorf CH-8600, Switzerland
| | - Erfu Wu
- Empa-Swiss Federal Laboratories for Materials Science and Technology, Dübendorf CH-8600, Switzerland
| | - Ivan Shorubalko
- Empa-Swiss Federal Laboratories for Materials Science and Technology, Dübendorf CH-8600, Switzerland
| | - Maksym V. Kovalenko
- Department of Chemistry and Applied Biosciences, Institute of Inorganic Chemistry, ETH Zürich, Zürich CH-8093, Switzerland
- Empa-Swiss Federal Laboratories for Materials Science and Technology, Dübendorf CH-8600, Switzerland
| | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza L. da Vinci 32, Milano 20133, Italy
| |
Collapse
|
31
|
Tarkov M, Tikhonenko F, Popov V, Antonov V, Miakonkikh A, Rudenko K. Ferroelectric Devices for Content-Addressable Memory. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:4488. [PMID: 36558341 PMCID: PMC9785747 DOI: 10.3390/nano12244488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/10/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
In-memory computing is an attractive solution for reducing power consumption and memory access latency cost by performing certain computations directly in memory without reading operands and sending them to arithmetic logic units. Content-addressable memory (CAM) is an ideal way to smooth out the distinction between storage and processing, since each memory cell is a processing unit. CAM compares the search input with a table of stored data and returns the matched data address. The issues of constructing binary and ternary content-addressable memory (CAM and TCAM) based on ferroelectric devices are considered. A review of ferroelectric materials and devices is carried out, including on ferroelectric transistors (FeFET), ferroelectric tunnel diodes (FTJ), and ferroelectric memristors.
Collapse
Affiliation(s)
- Mikhail Tarkov
- Rzhanov Institute of Semiconductor Physics SB RAS, 630090 Novosibirsk, Russia
| | - Fedor Tikhonenko
- Rzhanov Institute of Semiconductor Physics SB RAS, 630090 Novosibirsk, Russia
| | - Vladimir Popov
- Rzhanov Institute of Semiconductor Physics SB RAS, 630090 Novosibirsk, Russia
| | - Valentin Antonov
- Rzhanov Institute of Semiconductor Physics SB RAS, 630090 Novosibirsk, Russia
| | - Andrey Miakonkikh
- Valiev Institute of Physics and Technology RAS, 117218 Moscow, Russia
| | | |
Collapse
|
32
|
Subbulakshmi Radhakrishnan S, Chakrabarti S, Sen D, Das M, Schranghamer TF, Sebastian A, Das S. A Sparse and Spike-Timing-Based Adaptive Photoencoder for Augmenting Machine Vision for Spiking Neural Networks. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202535. [PMID: 35674268 DOI: 10.1002/adma.202202535] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/31/2022] [Indexed: 06/15/2023]
Abstract
The representation of external stimuli in the form of action potentials or spikes constitutes the basis of energy efficient neural computation that emerging spiking neural networks (SNNs) aspire to imitate. With recent evidence suggesting that information in the brain is more often represented by explicit firing times of the neurons rather than mean firing rates, it is imperative to develop novel hardware that can accelerate sparse and spike-timing-based encoding. Here a medium-scale integrated circuit composed of two cascaded three-stage inverters and one XOR logic gate fabricated using a total of 21 memtransistors based on photosensitive 2D monolayer MoS2 for spike-timing-based encoding of visual information, is introduced. It is shown that different illumination intensities can be encoded into sparse spiking with time-to-first-spike representing the illumination information, that is, higher intensities invoke earlier spikes and vice versa. In addition, non-volatile and analog programmability in the photoencoder is exploited for adaptive photoencoding that allows expedited spiking under scotopic (low-light) and deferred spiking under photopic (bright-light) conditions, respectively. Finally, low energy expenditure of less than 1 µJ by the 2D-memtransistor-based photoencoder highlights the benefits of in-sensor and bioinspired design that can be transformative for the acceleration of SNNs.
Collapse
Affiliation(s)
| | - Shakya Chakrabarti
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
| | - Dipanjan Sen
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Mayukh Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Thomas F Schranghamer
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Amritanand Sebastian
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA
| |
Collapse
|
33
|
Wang Y, Zhang Q, Astier HPAG, Nickle C, Soni S, Alami FA, Borrini A, Zhang Z, Honnigfort C, Braunschweig B, Leoncini A, Qi DC, Han Y, Del Barco E, Thompson D, Nijhuis CA. Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour. NATURE MATERIALS 2022; 21:1403-1411. [PMID: 36411348 DOI: 10.1038/s41563-022-01402-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
To realize molecular-scale electrical operations beyond the von Neumann bottleneck, new types of multifunctional switches are needed that mimic self-learning or neuromorphic computing by dynamically toggling between multiple operations that depend on their past. Here, we report a molecule that switches from high to low conductance states with massive negative memristive behaviour that depends on the drive speed and number of past switching events, with all the measurements fully modelled using atomistic and analytical models. This dynamic molecular switch emulates synaptic behavior and Pavlovian learning, all within a 2.4-nm-thick layer that is three orders of magnitude thinner than a neuronal synapse. The dynamic molecular switch provides all the fundamental logic gates necessary for deep learning because of its time-domain and voltage-dependent plasticity. The synapse-mimicking multifunctional dynamic molecular switch represents an adaptable molecular-scale hardware operable in solid-state devices, and opens a pathway to simplify dynamic complex electrical operations encoded within a single ultracompact component.
Collapse
Affiliation(s)
- Yulong Wang
- Department of Chemistry, National University of Singapore, Singapore, Singapore
| | - Qian Zhang
- Department of Chemistry, National University of Singapore, Singapore, Singapore
- School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, China
| | | | - Cameron Nickle
- Department of Physics, University of Central Florida, Orlando, FL, USA
| | - Saurabh Soni
- Hybrid Materials for Opto-Electronics Group, Department of Molecules and Materials, MESA+Institute for Nanotechnology, Molecules Center and Center for Brain-Inspired Nano Systems, Faculty of Science and Technology, University of Twente, Enschede, the Netherlands
| | - Fuad A Alami
- Hybrid Materials for Opto-Electronics Group, Department of Molecules and Materials, MESA+Institute for Nanotechnology, Molecules Center and Center for Brain-Inspired Nano Systems, Faculty of Science and Technology, University of Twente, Enschede, the Netherlands
| | - Alessandro Borrini
- Hybrid Materials for Opto-Electronics Group, Department of Molecules and Materials, MESA+Institute for Nanotechnology, Molecules Center and Center for Brain-Inspired Nano Systems, Faculty of Science and Technology, University of Twente, Enschede, the Netherlands
| | - Ziyu Zhang
- Department of Chemistry, National University of Singapore, Singapore, Singapore
| | - Christian Honnigfort
- Institute of Physical Chemistry, Westfälische Wilhelms-Universität Münster, Münster, Germany
- Center of Soft Nanoscience, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Björn Braunschweig
- Institute of Physical Chemistry, Westfälische Wilhelms-Universität Münster, Münster, Germany
- Center of Soft Nanoscience, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Andrea Leoncini
- Department of Chemistry, National University of Singapore, Singapore, Singapore
| | - Dong-Cheng Qi
- School of Chemistry and Physics, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Yingmei Han
- Department of Chemistry, National University of Singapore, Singapore, Singapore
| | - Enrique Del Barco
- Department of Physics, University of Central Florida, Orlando, FL, USA.
| | - Damien Thompson
- Department of Physics, Bernal Institute, University of Limerick, Limerick, Ireland.
| | - Christian A Nijhuis
- Department of Chemistry, National University of Singapore, Singapore, Singapore.
- Hybrid Materials for Opto-Electronics Group, Department of Molecules and Materials, MESA+Institute for Nanotechnology, Molecules Center and Center for Brain-Inspired Nano Systems, Faculty of Science and Technology, University of Twente, Enschede, the Netherlands.
| |
Collapse
|
34
|
Yang Y, Wu Y, He W, Tien H, Yang W, Michinobu T, Chen W, Lee W, Chueh C. Tuning Ambipolarity of the Conjugated Polymer Channel Layers of Floating-Gate Free Transistors: From Volatile Memories to Artificial Synapses. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203025. [PMID: 35986439 PMCID: PMC9631064 DOI: 10.1002/advs.202203025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/24/2022] [Indexed: 05/22/2023]
Abstract
Three-terminal synaptic transistor has drawn significant research interests for neuromorphic computation due to its advantage of facile device integrability. Lately, bulk-heterojunction-based synaptic transistors with bipolar modulation are proposed to exempt the use of an additional floating gate. However, the actual correlation between the channel's ambipolarity, memory characteristic, and synaptic behavior for a floating-gate free transistor has not been investigated yet. Herein, by studying five diketopyrrolopyrrole-benzotriazole dual-acceptor random conjugated polymers, a clear correlation among the hole/electron ratio, the memory retention characteristic, and the synaptic behavior for the polymer channel layer in a floating-gate free transistor is described. It reveals that the polymers with balanced ambipolarity possess better charge trapping capabilities and larger memory windows; however, the high ambipolarity results in higher volatility of the memory characteristics, namely poor memory retention capability. In contrast, the polymer with a reduced ambipolarity possesses an enhanced memory retention capability despite showing a reduced memory window. It is further manifested that this enhanced charge retention capability enables the device to present artificial synaptic characteristics. The results highlight the importance of the channel's ambipolarity of floating-gate free transistors on the resultant volatile memory characteristics and synaptic behaviors.
Collapse
Affiliation(s)
- Yu‐Ting Yang
- Department of Chemical EngineeringNational Taiwan UniversityTaipei10617Taiwan
| | - Ying‐Sheng Wu
- Department of Chemical EngineeringNational Taiwan UniversityTaipei10617Taiwan
| | - Waner He
- Department of Materials Science and EngineeringTokyo Institute of Technology2‐12‐1 Ookayama, Meguro‐kuTokyo152‐8552Japan
| | - Hsin‐Chiao Tien
- Research and Development Center for Smart Textile Technology and Department of Chemical Engineering and BiotechnologyNational Taipei University of TechnologyTaipei106Taiwan
| | - Wei‐Chen Yang
- Department of Chemical EngineeringNational Taiwan UniversityTaipei10617Taiwan
- Advanced Research Center of Green Materials Science and TechnologyNational Taiwan UniversityTaipei10617Taiwan
| | - Tsuyoshi Michinobu
- Department of Materials Science and EngineeringTokyo Institute of Technology2‐12‐1 Ookayama, Meguro‐kuTokyo152‐8552Japan
| | - Wen‐Chang Chen
- Department of Chemical EngineeringNational Taiwan UniversityTaipei10617Taiwan
- Advanced Research Center of Green Materials Science and TechnologyNational Taiwan UniversityTaipei10617Taiwan
| | - Wen‐Ya Lee
- Research and Development Center for Smart Textile Technology and Department of Chemical Engineering and BiotechnologyNational Taipei University of TechnologyTaipei106Taiwan
| | - Chu‐Chen Chueh
- Department of Chemical EngineeringNational Taiwan UniversityTaipei10617Taiwan
- Advanced Research Center of Green Materials Science and TechnologyNational Taiwan UniversityTaipei10617Taiwan
| |
Collapse
|
35
|
Zha C, Luo W, Zhang X, Yan X, Ren X. Low-Consumption Synaptic Devices Based on Gate-All-Around InAs Nanowire Field-Effect Transistors. NANOSCALE RESEARCH LETTERS 2022; 17:101. [PMID: 36301382 PMCID: PMC9613821 DOI: 10.1186/s11671-022-03740-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
In this work, an artificial electronic synaptic device based on gate-all-around InAs nanowire field-effect transistor is proposed and analyzed. The deposited oxide layer (In2O3) on the InAs nanowire surface serves as a charge trapping layer for information storage. The gate voltage pulse serves as stimuli of the presynaptic membrane, and the drain current and channel conductance are treated as post-synaptic current and weights of the postsynaptic membrane, respectively. At low gate voltages, the device simulates synaptic behaviors including short-term depression and long-term depression. By increasing the amplitude and quantity of gate voltage pulses, the transition from short-term depression to long-term potentiation can be achieved. The device exhibits a large memory window of over 1 V and a minimal energy consumption of 12.5 pJ per synaptic event. This work may pave the way for the development of miniaturized low-consumption synaptic devices and related neuromorphic systems.
Collapse
Affiliation(s)
- Chaofei Zha
- State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Wei Luo
- State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Xia Zhang
- State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
| | - Xin Yan
- State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
| | - Xiaomin Ren
- State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| |
Collapse
|
36
|
Min JG, Park H, Cho WJ. Milk-Ta 2O 5 Hybrid Memristors with Crossbar Array Structure for Bio-Organic Neuromorphic Chip Applications. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:2978. [PMID: 36080015 PMCID: PMC9457690 DOI: 10.3390/nano12172978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/17/2022] [Accepted: 08/22/2022] [Indexed: 06/15/2023]
Abstract
In this study, a high-performance bio-organic memristor with a crossbar array structure using milk as a resistive switching layer (RSL) is proposed. To ensure compatibility with the complementary metal oxide semiconductor process of milk RSL, a high-k Ta2O5 layer was deposited as a capping layer; this layer enables high-density, integration-capable, photolithography processes. The fabricated crossbar array memristors contain milk-Ta2O5 hybrid membranes, and they exhibit bipolar resistance switching behavior and uniform resistance distribution across hundreds of repeated test cycles. In terms of the artificial synaptic behavior and synaptic weight changes, milk-Ta2O5 hybrid crossbar array memristors have a stable analog RESET process, and the memristors are highly responsive to presynaptic stimulation via paired-pulse facilitation excitatory post-synaptic current. Moreover, spike-timing-dependent plasticity and potentiation and depression behaviors, which closely emulate long-term plasticity and modulate synaptic weights, were evaluated. Finally, an artificial neural network was designed and trained to recognize the pattern of the Modified National Institute of Standards and Technology (MNIST) digits to evaluate the capability of the neuromorphic computing system. Consequently, a high recognition rate of over 88% was achieved. Thus, the milk-Ta2O5 hybrid crossbar array memristor is a promising electronic platform for in-memory computing systems.
Collapse
Affiliation(s)
- Jin-Gi Min
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Korea
| | - Hamin Park
- Department of Electronic Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Korea
| | - Won-Ju Cho
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Korea
| |
Collapse
|
37
|
Nikam RD, Lee J, Choi W, Kim D, Hwang H. On-Chip Integrated Atomically Thin 2D Material Heater as a Training Accelerator for an Electrochemical Random-Access Memory Synapse for Neuromorphic Computing Application. ACS NANO 2022; 16:12214-12225. [PMID: 35853220 DOI: 10.1021/acsnano.2c02913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
An artificial synapse based on oxygen-ion-driven electrochemical random-access memory (O-ECRAM) devices is a promising candidate for building neural networks embodied in neuromorphic hardware. However, achieving commercial-level learning accuracy in O-ECRAM synapses, analog conductance tuning at fast speed, and multibit storage capacity is challenging because of the lack of Joule heating, which restricts O2- ionic transport. Here, we propose the use of an atomically thin heater of monolayer graphene as a low-power heating source for O-ECRAM to increase thermally activated O2- migration within channel-electrolyte layers. Heating from graphene manipulates the electrolyte activation energy to establish and maintain discrete analog states in the O-ECRAM channel. Benefiting from the integrated graphene heater, the O-ECRAM features long retention (>104 s), good stability (switching accuracy <98% for >103 training pulses), multilevel analog states for 6-bit analog weight storage with near-ideal linear switching, and 95% pattern-identification accuracy. The findings demonstrate the usefulness of 2D materials as integrated heating elements in artificial synapse chips to accelerate neuromorphic computation.
Collapse
|
38
|
Chee MY, Dananjaya PA, Lim GJ, Du Y, Lew WS. Frequency-Dependent Synapse Weight Tuning in 1S1R with a Short-Term Plasticity TiO x-Based Exponential Selector. ACS APPLIED MATERIALS & INTERFACES 2022; 14:35959-35968. [PMID: 35892238 DOI: 10.1021/acsami.2c11016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Short-term plasticity (STP) is an important synaptic characteristic in the hardware implementation of artificial neural networks (ANN), as it enables the temporal information processing (TIP) capability. However, the STP feature is rather challenging to reproduce from a single nonvolatile resistive random-access memory (RRAM) element, as it requires a certain degree of volatility. In this work, a Pt/TiOx/Pt exponential selector is introduced not only to suppress the sneak current but also to enable the TIP feature in a one selector-one RRAM (1S1R) synaptic device. Our measurements reveal that the exponential selector exhibits the STP characteristic, while a Pt/HfOx/Ti RRAM enables the long-term memory capability of the synapse. Thereafter, we experimentally demonstrated pulse frequency-dependent multilevel switching in the 1S1R device, exhibiting the TIP capability of the developed 1S1R synapse. The observed STP of the selector is strongly influenced by the bottom metal-oxide interface, in which Ar plasma treatment on the bottom Pt electrode resulted in the annihilation of the STP feature in the selector. A mechanism is thus proposed to explain the observed STP, using the local electric field enhancement induced at the metal-oxide interface coupled with the drift-diffusion model of mobile O2- and Ti3+ ions. This work therefore provides a reliable means of producing the STP feature in a 1S1R device, which demonstrates the TIP capability sought after in hardware-based ANN.
Collapse
Affiliation(s)
- Mun Yin Chee
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
| | - Putu Andhita Dananjaya
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
| | - Gerard Joseph Lim
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
| | - Yuanmin Du
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
| | - Wen Siang Lew
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
| |
Collapse
|
39
|
Barman A, Das D, Deshmukh S, Sarkar PK, Banerjee D, Hübner R, Gupta M, Saini CP, Kumar S, Johari P, Dhar S, Kanjilal A. Aliovalent Ta-Doping-Engineered Oxygen Vacancy Configurations for Ultralow-Voltage Resistive Memory Devices: A DFT-Supported Experimental Study. ACS APPLIED MATERIALS & INTERFACES 2022; 14:34822-34834. [PMID: 35866235 DOI: 10.1021/acsami.2c05089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Alteration of transport properties of any material, especially metal oxides, by doping suitable impurities is not straightforward as it may introduce multiple defects like oxygen vacancies (Vo) in the system. It plays a decisive role in controlling the resistive switching (RS) performance of metal oxide-based memory devices. Therefore, a judicious choice of dopants and their atomic concentrations is crucial for achieving an optimum Vo configuration. Here, we show that the rational designing of RS memory devices with cationic dopants (Ta), in particular, Au/Ti1-xTaxO2-δ/Pt devices, is promising for the upcoming non-volatile memory technology. Indeed, a current window of ∼104 is realized at an ultralow voltage as low as 0.25 V with significant retention (∼104 s) and endurance (∼105 cycles) of the device by considering 1.11 at % Ta doping. The obtained device parameters are compared with those in the available literature to establish its excellent performance. Furthermore, using detailed experimental analyses and density functional theory (DFT)-based first-principles calculations, we comprehend that the meticulous presence of Vo configurations and the columnar-like dendritic structures is crucial for achieving ultralow-voltage bipolar RS characteristics. In fact, the dopant-mediated Vo interactions are found to be responsible for the enhancement in local current conduction, as evidenced from the DFT-simulated electron localization function plots, and these, in turn, augment the device performance. Overall, the present study on cationic-dopant-controlled defect engineering could pave a neoteric direction for future energy-efficient oxide memristors.
Collapse
Affiliation(s)
- Arabinda Barman
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
- Department of Physics, Dinhata College, Dinhata, West Bengal 736 135, India
| | - Dip Das
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - Sujit Deshmukh
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - Pranab Kumar Sarkar
- Department of Applied Sciences and Humanities, Assam University, Silchar, Assam 788 011, India
| | - Debosmita Banerjee
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - René Hübner
- Institute of Ion Beam Physics and Materials Research, Helmholtz-Zentrum Dresden-Rossendorf, Dersden 01328, Germany
| | - Mukul Gupta
- UGC-DAE Consortium for Scientific Research, Khandwa Road, Indore, Madhya Pradesh 452 001, India
| | | | - Shammi Kumar
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - Priya Johari
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - Sankar Dhar
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - Aloke Kanjilal
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| |
Collapse
|
40
|
Wang J, Zhu Y, Zhu L, Chen C, Wan Q. Emerging Memristive Devices for Brain-Inspired Computing and Artificial Perception. FRONTIERS IN NANOTECHNOLOGY 2022. [DOI: 10.3389/fnano.2022.940825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Brain-inspired computing is an emerging field that aims at building a compact and massively parallel architecture, to reduce power consumption in conventional Von Neumann Architecture. Recently, memristive devices have gained great attention due to their immense potential in implementing brain-inspired computing and perception. The conductance of a memristor can be modulated by a voltage pulse, enabling emulations of both essential synaptic and neuronal functions, which are considered as the important building blocks for artificial neural networks. As a result, it is critical to review recent developments of memristive devices in terms of neuromorphic computing and perception applications, waiting for new thoughts and breakthroughs. The device structures, operation mechanisms, and materials are introduced sequentially in this review; additionally, late advances in emergent neuromorphic computing and perception based on memristive devices are summed up. Finally, the challenges that memristive devices toward high-performance brain-inspired computing and perception are also briefly discussed. We believe that the advances and challenges will lead to significant advancements in artificial neural networks and intelligent humanoid robots.
Collapse
|
41
|
Ismail M, Mahata C, Kang M, Kim S. Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering. NANOSCALE RESEARCH LETTERS 2022; 17:61. [PMID: 35749003 PMCID: PMC9232664 DOI: 10.1186/s11671-022-03699-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 06/14/2022] [Indexed: 06/15/2023]
Abstract
For neuromorphic computing and high-density data storage memory, memristive devices have recently gained a lot of interest. So far, memristive devices have suffered from switching parameter instability, such as distortions in resistance values of low- and high-resistance states (LRSs and HRSs), dispersion in working voltage (set and reset voltages), and a small ratio of high and low resistance, among other issues. In this context, interface engineering is a critical technique for addressing the variation issues that obstruct the use of memristive devices. Herein, we engineered a high band gap, low Gibbs free energy Al2O3 interlayer between the HfO2 switching layer and the tantalum oxy-nitride electrode (TaN) bottom electrode to operate as an oxygen reservoir, increasing the resistance ratio between HRS and LRS and enabling multilayer data storage. The Pt/HfO2/Al2O3/TaN memristive device demonstrates analog bipolar resistive switching behavior with a potential ratio of HRS and LRS of > 105 and the ability to store multi-level data with consistent retention and uniformity. On set and reset voltages, statistical analysis is used; the mean values (µ) of set and reset voltages are determined to be - 2.7 V and + 1.9 V, respectively. There is a repeatable durability over DC 1000 cycles, 105 AC cycles, and a retention time of 104 s at room temperature. Quantum conductance was obtained by increasing the reset voltage with step of 0.005 V with delay time of 0.1 s. Memristive device has also displayed synaptic properties like as potentiation/depression and paired-pulse facilitation (PPF). Results show that engineering of interlayer is an effective approach to improve the uniformity, ratio of high and low resistance, and multiple conductance quantization states and paves the way for research into neuromorphic synapses.
Collapse
Affiliation(s)
- Muhammad Ismail
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Chandreswar Mahata
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Myounggon Kang
- Department of Electronics Engineering, Korea National University of Transportation, Chungju-si, 27469, Republic of Korea.
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea.
| |
Collapse
|
42
|
Makarov VA, Lobov SA, Shchanikov S, Mikhaylov A, Kazantsev VB. Toward Reflective Spiking Neural Networks Exploiting Memristive Devices. Front Comput Neurosci 2022; 16:859874. [PMID: 35782090 PMCID: PMC9243340 DOI: 10.3389/fncom.2022.859874] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022] Open
Abstract
The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.
Collapse
Affiliation(s)
- Valeri A. Makarov
- Instituto de Matemática Interdisciplinar, Universidad Complutense de Madrid, Madrid, Spain
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- *Correspondence: Valeri A. Makarov,
| | - Sergey A. Lobov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Sergey Shchanikov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Department of Information Technologies, Vladimir State University, Vladimir, Russia
| | - Alexey Mikhaylov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Viktor B. Kazantsev
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| |
Collapse
|
43
|
Liu F, Deswal S, Christou A, Sandamirskaya Y, Kaboli M, Dahiya R. Neuro-inspired electronic skin for robots. Sci Robot 2022; 7:eabl7344. [PMID: 35675450 DOI: 10.1126/scirobotics.abl7344] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.
Collapse
Affiliation(s)
- Fengyuan Liu
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Sweety Deswal
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Adamos Christou
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | | | - Mohsen Kaboli
- Department of Research, New Technologies, Innovation, BMW Group, Parkring 19, 85748 Garching bei Munchen, Germany.,Cognitive Robotics and Tactile Intelligence Group, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Ravinder Dahiya
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| |
Collapse
|
44
|
Choi HW, Song KW, Kim SH, Nguyen KT, Eadi SB, Kwon HM, Lee HD. Zinc oxide and indium-gallium-zinc-oxide bi-layer synaptic device with highly linear long-term potentiation and depression characteristics. Sci Rep 2022; 12:1259. [PMID: 35075173 PMCID: PMC8786833 DOI: 10.1038/s41598-022-05150-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 01/04/2022] [Indexed: 11/09/2022] Open
Abstract
The electrical properties, resistive switching behavior, and long-term potentiation/depression (LTP/LTD) in a single indium-gallium-zinc-oxide (IGZO) and bi-layer IGZO/ZnO (ZnO: zinc oxide) memristors were investigated for synapse application. The use of the oxide bi-layer memristors, in particular, improved electrical properties such as stability, memristor reliability, and an increase in synaptic weight states. The set voltage of bi-layer IGZO/ZnO memristors was 0.9 V, and the reset voltage was around - 0.7 V, resulting in a low-operating voltage for neuromorphic systems. The oxygen vacancies in the X-ray photoelectron spectroscopy analysis played a role in the modulation of the high-resistance state (HRS) (oxygen-deficient) and the low-resistance state (oxygen-rich) region. The VRESET of the bi-layer IGZO/ZnO memristors was lower than that of a single IGZO, which implied that oxygen-vacancy filaments could be easily ruptured due to the higher oxygen vacancy peak HRS layer. The nonlinearity of the LTP and LTD characteristics in a bi-layer IGZO/ZnO memristor was 6.77% and 11.49%, respectively, compared to those of 20.03% and 51.1% in a single IGZO memristor, respectively. Therefore, the extra ZnO layer in the bi-layer memristor with IGZO was potentially significant and essential to achieve a small set voltage and a reset voltage, and the switching behavior to form the conductive path.
Collapse
Affiliation(s)
- Hyun-Woong Choi
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Ki-Woo Song
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Seong-Hyun Kim
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Kim Thanh Nguyen
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Sunil Babu Eadi
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Hyuk-Min Kwon
- Department of Semiconductor Processing Equipment, Semiconductor Convergence Campus of Korea Polytechnic College, 41-12, Songwon-Gil, Kongdo-Eup, Anseong, Kyunggi-Do, Republic of Korea.
| | - Hi-Deok Lee
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.
| |
Collapse
|
45
|
Sun B, Zhou G, Sun L, Zhao H, Chen Y, Yang F, Zhao Y, Song Q. ABO 3 multiferroic perovskite materials for memristive memory and neuromorphic computing. NANOSCALE HORIZONS 2021; 6:939-970. [PMID: 34652346 DOI: 10.1039/d1nh00292a] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The unique electron spin, transfer, polarization and magnetoelectric coupling characteristics of ABO3 multiferroic perovskite materials make them promising candidates for application in multifunctional nanoelectronic devices. Reversible ferroelectric polarization, controllable defect concentration and domain wall movement originated from the ABO3 multiferroic perovskite materials promotes its memristive effect, which further highlights data storage, information processing and neuromorphic computing in diverse artificial intelligence applications. In particular, ion doping, electrode selection, and interface modulation have been demonstrated in ABO3-based memristive devices for ultrahigh data storage, ultrafast information processing, and efficient neuromorphic computing. These approaches presented today including controlling the dopant in the active layer, altering the oxygen vacancy distribution, modulating the diffusion depth of ions, and constructing the interface-dependent band structure were believed to be efficient methods for obtaining unique resistive switching (RS) behavior for various applications. In this review, internal physical dynamics, preparation technologies, and modulation methods are systemically examined as well as the progress, challenges, and possible solutions are proposed for next generation emerging ABO3-based memristive application in artificial intelligence.
Collapse
Affiliation(s)
- Bai Sun
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials (Ministry of Education of China), Southwest Jiaotong University, Chengdu, Sichuan 610031, China.
- Superconductivity and New Energy R&D Center, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Guangdong Zhou
- School of Artificial Intelligence and School of Materials and Energy, Southwest University, Chongqing 400715, China.
| | - Linfeng Sun
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
| | - Hongbin Zhao
- State Key Laboratory of Advanced Materials for Smart Sensing, General Research Institute for Nonferrous Metals, Beijing, 100088, China
| | - Yuanzheng Chen
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials (Ministry of Education of China), Southwest Jiaotong University, Chengdu, Sichuan 610031, China.
| | - Feng Yang
- Superconductivity and New Energy R&D Center, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Yong Zhao
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials (Ministry of Education of China), Southwest Jiaotong University, Chengdu, Sichuan 610031, China.
- Superconductivity and New Energy R&D Center, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Qunliang Song
- School of Artificial Intelligence and School of Materials and Energy, Southwest University, Chongqing 400715, China.
| |
Collapse
|
46
|
Bian H, Goh YY, Liu Y, Ling H, Xie L, Liu X. Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2006469. [PMID: 33837601 DOI: 10.1002/adma.202006469] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 12/03/2020] [Indexed: 06/12/2023]
Abstract
Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.
Collapse
Affiliation(s)
- Hongyu Bian
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
| | - Yi Yiing Goh
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, 119077, Singapore
| | - Yuxia Liu
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
- Center for Functional Materials, National University of Singapore Suzhou Research Institute, Suzhou, 215123, China
| | - Haifeng Ling
- Key Laboratory for Organic Electronics and Information Displays and Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Linghai Xie
- Key Laboratory for Organic Electronics and Information Displays and Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Xiaogang Liu
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
- Center for Functional Materials, National University of Singapore Suzhou Research Institute, Suzhou, 215123, China
| |
Collapse
|
47
|
Min JG, Cho WJ. Chitosan-Based Flexible Memristors with Embedded Carbon Nanotubes for Neuromorphic Electronics. MICROMACHINES 2021; 12:1259. [PMID: 34683310 PMCID: PMC8541661 DOI: 10.3390/mi12101259] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 10/13/2021] [Accepted: 10/13/2021] [Indexed: 11/16/2022]
Abstract
In this study, we propose high-performance chitosan-based flexible memristors with embedded single-walled carbon nanotubes (SWCNTs) for neuromorphic electronics. These flexible transparent memristors were applied to a polyethylene naphthalate (PEN) substrate using low-temperature solution processing. The chitosan-based flexible memristors have a bipolar resistive switching (BRS) behavior due to the cation-based electrochemical reaction between a polymeric chitosan electrolyte and mobile ions. The effect of SWCNT addition on the BRS characteristics was analyzed. It was observed that the embedded SWCNTs absorb more metal ions and trigger the conductive filament in the chitosan electrolyte, resulting in a more stable and wider BRS window compared to the device with no SWCNTs. The memory window of the chitosan nanocomposite memristors with SWCNTs was 14.98, which was approximately double that of devices without SWCNTs (6.39). Furthermore, the proposed SWCNT-embedded chitosan-based memristors had memristive properties, such as short-term and long-term plasticity via paired-pulse facilitation and spike-timing-dependent plasticity, respectively. In addition, the conductivity modulation was evaluated with 300 synaptic pulses. These findings suggest that memristors featuring SWCNT-embedded chitosan are a promising building block for future artificial synaptic electronics applications.
Collapse
Affiliation(s)
| | - Won-Ju Cho
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Korea;
| |
Collapse
|
48
|
Zhevnenko DA, Meshchaninov FP, Kozhevnikov VS, Shamin ES, Telminov OA, Gornev ES. Research and Development of Parameter Extraction Approaches for Memristor Models. MICROMACHINES 2021; 12:mi12101220. [PMID: 34683271 PMCID: PMC8538760 DOI: 10.3390/mi12101220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 11/16/2022]
Abstract
Memristors are among the most promising devices for building neural processors and non-volatile memory. One circuit design stage involves modeling, which includes the option of memristor models. The most common approach is the use of compact models, the accuracy of which is often determined by the accuracy of their parameter extraction from experiment results. In this paper, a review of existing extraction methods was performed and new parameter extraction algorithms for an adaptive compact model were proposed. The effectiveness of the developed methods was confirmed for the volt-ampere characteristic of a memristor with a vertical structure: TiN/HfxAl1-xOy/HfO2/TiN.
Collapse
Affiliation(s)
- Dmitry Alexeevich Zhevnenko
- Moscow Institute of Physics and Technology, 9 Institutskiy per., Dolgoprudny, 141701 Moscow, Russia; (D.A.Z.); (V.S.K.); (E.S.S.); (O.A.T.); (E.S.G.)
- Joint-Stock Company “Molecular Electronics Research Institute” (JSC MERI), 12/1 1st Zapadnyi Proezd, Zelenograd, 124460 Moscow, Russia
| | - Fedor Pavlovich Meshchaninov
- Moscow Institute of Physics and Technology, 9 Institutskiy per., Dolgoprudny, 141701 Moscow, Russia; (D.A.Z.); (V.S.K.); (E.S.S.); (O.A.T.); (E.S.G.)
- Joint-Stock Company “Molecular Electronics Research Institute” (JSC MERI), 12/1 1st Zapadnyi Proezd, Zelenograd, 124460 Moscow, Russia
- Correspondence: ; Tel.: +7-915-3604-969
| | - Vladislav Sergeevich Kozhevnikov
- Moscow Institute of Physics and Technology, 9 Institutskiy per., Dolgoprudny, 141701 Moscow, Russia; (D.A.Z.); (V.S.K.); (E.S.S.); (O.A.T.); (E.S.G.)
- Joint-Stock Company “Molecular Electronics Research Institute” (JSC MERI), 12/1 1st Zapadnyi Proezd, Zelenograd, 124460 Moscow, Russia
| | - Evgeniy Sergeevich Shamin
- Moscow Institute of Physics and Technology, 9 Institutskiy per., Dolgoprudny, 141701 Moscow, Russia; (D.A.Z.); (V.S.K.); (E.S.S.); (O.A.T.); (E.S.G.)
- Joint-Stock Company “Molecular Electronics Research Institute” (JSC MERI), 12/1 1st Zapadnyi Proezd, Zelenograd, 124460 Moscow, Russia
| | - Oleg Alexandrovich Telminov
- Moscow Institute of Physics and Technology, 9 Institutskiy per., Dolgoprudny, 141701 Moscow, Russia; (D.A.Z.); (V.S.K.); (E.S.S.); (O.A.T.); (E.S.G.)
- Joint-Stock Company “Molecular Electronics Research Institute” (JSC MERI), 12/1 1st Zapadnyi Proezd, Zelenograd, 124460 Moscow, Russia
| | - Evgeniy Sergeevich Gornev
- Moscow Institute of Physics and Technology, 9 Institutskiy per., Dolgoprudny, 141701 Moscow, Russia; (D.A.Z.); (V.S.K.); (E.S.S.); (O.A.T.); (E.S.G.)
- Joint-Stock Company “Molecular Electronics Research Institute” (JSC MERI), 12/1 1st Zapadnyi Proezd, Zelenograd, 124460 Moscow, Russia
| |
Collapse
|
49
|
Wang Z, Wang L, Wu Y, Bian L, Nagai M, Jv R, Xie L, Ling H, Li Q, Bian H, Yi M, Shi N, Liu X, Huang W. Signal Filtering Enabled by Spike Voltage-Dependent Plasticity in Metalloporphyrin-Based Memristors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2104370. [PMID: 34510593 DOI: 10.1002/adma.202104370] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/25/2021] [Indexed: 06/13/2023]
Abstract
Neural systems can selectively filter and memorize spatiotemporal information, thus enabling high-efficient information processing. Emulating such an exquisite biological process in electronic devices is of fundamental importance for developing neuromorphic architectures with efficient in situ edge/parallel computing, and probabilistic inference. Here a novel multifunctional memristor is proposed and demonstrated based on metalloporphyrin/oxide hybrid heterojunction, in which the metalloporphyrin layer allows for dual electronic/ionic transport. Benefiting from the coordination-assisted ionic diffusion, the device exhibits smooth, gradual conductive transitions. It is shown that the memristive characteristics of this hybrid system can be modulated by altering the metal center for desired metal-oxygen bonding energy and oxygen ions migration dynamics. The spike voltage-dependent plasticity stemming from the local/extended movement of oxygen ions under low/high voltage is identified, which permits potentiation and depression under unipolar different positive voltages. As a proof-of-concept demonstration, memristive arrays are further built to emulate the signal filtering function of the biological visual system. This work demonstrates the ionic intelligence feature of metalloporphyrin and paves the way for implementing efficient neural-signal analysis in neuromorphic hardware.
Collapse
Affiliation(s)
- Zhiyong Wang
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Laiyuan Wang
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Yiming Wu
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, Singapore, 138634, Singapore
| | - Linyi Bian
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Masaru Nagai
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), Nanjing, 211816, China
| | - Ruolin Jv
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Linghai Xie
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Haifeng Ling
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Qi Li
- Physical Science Division, IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA
| | - Hongyu Bian
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
| | - Mingdong Yi
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Naien Shi
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Xiaogang Liu
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, Singapore, 138634, Singapore
- Joint School of National University of Singapore and Tianjin, University International Campus of Tianjin University, Fuzhou, 350207, China
| | - Wei Huang
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), Nanjing, 211816, China
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an, 710072, China
| |
Collapse
|
50
|
Yang K, Joshua Yang J, Huang R, Yang Y. Nonlinearity in Memristors for Neuromorphic Dynamic Systems. SMALL SCIENCE 2021. [DOI: 10.1002/smsc.202100049] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Ke Yang
- Department of Micro/nanoelectronics Peking University Beijing 100871 China
| | - J. Joshua Yang
- Electrical and Computer Engineering Department University of Southern California Los Angeles CA 90089 USA
| | - Ru Huang
- Department of Micro/nanoelectronics Peking University Beijing 100871 China
- Center for Brain Inspired Chips Institute for Artificial Intelligence Peking University Beijing 100871 China
- Center for Brain Inspired Intelligence Chinese Institute for Brain Research (CIBR) Beijing 102206 China
| | - Yuchao Yang
- Department of Micro/nanoelectronics Peking University Beijing 100871 China
- Center for Brain Inspired Chips Institute for Artificial Intelligence Peking University Beijing 100871 China
- Center for Brain Inspired Intelligence Chinese Institute for Brain Research (CIBR) Beijing 102206 China
| |
Collapse
|