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Lee Y, Huang Y, Chang YF, Yang SJ, Ignacio ND, Kutagulla S, Mohan S, Kim S, Lee J, Akinwande D, Kim S. Programmable Retention Characteristics in MoS 2-Based Atomristors for Neuromorphic and Reservoir Computing Systems. ACS NANO 2024; 18:14327-14338. [PMID: 38767980 DOI: 10.1021/acsnano.4c00333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
In this study, we investigate the coexistence of short- and long-term memory effects owing to the programmable retention characteristics of a two-dimensional Au/MoS2/Au atomristor device and determine the impact of these effects on synaptic properties. This device is constructed using bilayer MoS2 in a crossbar structure. The presence of both short- and long-term memory characteristics is proposed by using a filament model within the bilayer transition-metal dichalcogenide. Short- and long-term properties are validated based on programmable multilevel retention tests. Moreover, we confirm various synaptic characteristics of the device, demonstrating its potential use as a synaptic device in a neuromorphic system. Excitatory postsynaptic current, paired-pulse facilitation, spike-rate-dependent plasticity, and spike-number-dependent plasticity synaptic applications are implemented by operating the device at a low-conductance level. Furthermore, long-term potentiation and depression exhibit symmetrical properties at high-conductance levels. Synaptic learning and forgetting characteristics are emulated using programmable retention properties and composite synaptic plasticity. The learning process of artificial neural networks is used to achieve high pattern recognition accuracy, thereby demonstrating the suitability of the use of the device in a neuromorphic system. Finally, the device is used as a physical reservoir with time-dependent inputs to realize reservoir computing by using short-term memory properties. Our study reveals that the proposed device can be applied in artificial intelligence-based computing applications by utilizing its programmable retention properties.
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Affiliation(s)
- Yoonseok Lee
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Seoul 04620, Korea
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Yifu Huang
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Yao-Feng Chang
- Intel Corporation, Hillsboro, Oregon 97124, United States
| | - Sung Jin Yang
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Nicholas D Ignacio
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Shanmukh Kutagulla
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Sivasakthya Mohan
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Sunghun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Seoul 04620, Korea
| | - Jungwoo Lee
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Seoul 04620, Korea
| | - Deji Akinwande
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Seoul 04620, Korea
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2
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Li Z, Lin X, Wei J, Shan X, Wu Z, Luo Y, Wang Y, Feng Y, Ren T, Song Z, Wang F, Zhang K. An Artificial LiSiO x Nociceptor with Neural Blockade and Self-Protection Abilities. ACS APPLIED MATERIALS & INTERFACES 2024; 16:19205-19213. [PMID: 38591860 DOI: 10.1021/acsami.4c01406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
An artificial nociceptor, as a critical and special bionic receptor, plays a key role in a bioelectronic device that detects stimuli and provides warnings. However, fully exploiting bioelectronic applications remains a major challenge due to the lack of the methods of implementing basic nociceptor functions and nociceptive blockade in a single device. In this work, we developed a Pt/LiSiOx/TiN artificial nociceptor. It had excellent stability under the 104 endurance test with pulse stimuli and exhibited a significant threshold current of 1 mA with 1 V pulse stimuli. Other functions such as relaxation, inadaptation, and sensitization were all realized in a single device. Also, the pain blockade function was first achieved in this nociceptor with over a 25% blocking degree, suggesting a self-protection function. More importantly, an obvious depression was activated by a stimulus over 1.6 V due to the cooperative effects of both lithium ions and oxygen ions in LiSiOx and the dramatic accumulation of Joule heat. The conducting channel ruptured partially under sequential potentiation, thus achieving nociceptive blockade, besides basic functions in one single nociceptor, which was rarely reported. These results provided important guidelines for constructing high-performance memristor-based artificial nociceptors and opened up an alternative approach to the realization of bioelectronic systems for artificial intelligence.
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Affiliation(s)
- Zewen Li
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Xin Lin
- School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Junqing Wei
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Xin Shan
- School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Zeyu Wu
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Yu Luo
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Yuchan Wang
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Yulin Feng
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, China
| | - Tianling Ren
- Beijing National Research Center for Information Science and Technology, Institute of Microelectronics, Tsinghua University, Beijing 100084, China
| | - Zhitang Song
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
| | - Fang Wang
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Kailiang Zhang
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
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Shen W, Wang P, Wei G, Yuan S, Chen M, Su Y, Xu B, Li G. SiC@NiO Core-Shell Nanowire Networks-Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2400458. [PMID: 38607289 DOI: 10.1002/smll.202400458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/18/2024] [Indexed: 04/13/2024]
Abstract
1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200 °C. Based on the advanced functions under light stimulus, the constructed 5 × 3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200 °C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.
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Affiliation(s)
- Weikang Shen
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Pan Wang
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Guodong Wei
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering, Taiyuan, Shanxi, 030024, P. R. China
| | - Shuai Yuan
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Mi Chen
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Ying Su
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Bingshe Xu
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering, Taiyuan, Shanxi, 030024, P. R. China
| | - Guoqiang Li
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
- The School of Integrated Circuits, State Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou, 510641, P. R. China
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Wu T, Gao S, Li Y. IGZO/WO 3-x -Heterostructured Artificial Optoelectronic Synaptic Devices Mimicking Image Segmentation and Motion Capture. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2309857. [PMID: 38258604 DOI: 10.1002/smll.202309857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/11/2024] [Indexed: 01/24/2024]
Abstract
Currently, artificial neural networks (ANNs) based on memristors are limited to recognizing static images of objects when simulating human visual system, preventing them from performing high-dimensional information perception, and achieving more complex biomimetic functions is subject to certain limitations. In this work, indium gallium zinc oxide (IGZO)/tungsten oxide (WO3-x )-heterostructured artificial optoelectronic synaptic devices mimicking image segmentation and motion capture exhibiting high-performance optoelectronic synaptic responses are proposed and demonstrated. Upon electrical and optical stimulations, the device shows a variety of fundamental and advanced electrical and optical synaptic plasticity. Most importantly, outstanding and repeatable linear synaptic weight changes are attained by the developed memristor. By taking advantage of the notable linear synaptic weight changes, ANNs have been constructed and successfully utilized to demonstrate two applications in the field of computer vision, including image segmentation and object tracking. The accuracy attained by the memristor-based ANNs is similar to that of the computer algorithms, while its power has been significantly reduced by 105 orders of magnitude. With successful emulations of the human brain reactions when observing objects, the demonstrated memristor and related ANNs can be effectively utilized in constructing artificial optoelectronic synaptic devices and show promising potential in emulating human visual perception.
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Affiliation(s)
- Tong Wu
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, 250022, China
| | - Song Gao
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, 250022, China
| | - Yang Li
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, 250022, China
- School of Microelectronics, Shandong University, Jinan, 250101, China
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5
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Kim J, Park Y, Lee JK, Kim S. Preliminary investigation on the implementation of an artificial synapse using TaOx-based memristor with thermally oxidized active layer. J Chem Phys 2023; 159:214711. [PMID: 38054517 DOI: 10.1063/5.0182699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 11/14/2023] [Indexed: 12/07/2023] Open
Abstract
This study presents a preliminary exploration of thermally oxidized TaOx-based memristors and their potential as artificial synapses. Unlike the 10-min annealed devices, which display instability due to current overshoots, the 5-min annealed device exhibits stable resistive switching, retention, and endurance characteristics. Moreover, our memristor showcases synaptic behaviors encompassing potentiation, depression, spike-timing-dependent plasticity, and excitatory postsynaptic currents. This synaptic emulation holds tremendous promise for applications in neuromorphic computing, offering the opportunity to replicate the adaptive learning principles observed in biological synapses. In addition, we evaluate the device's suitability for pattern recognition within a neural network using the modified National Institute of Standards and Technology dataset. Our assessment reveals that the Pt/TaOx/Ta memristor with an oxidized insulator achieves outstanding potential manifested by an accuracy of 93.25% for the identical pulse scheme and an impressive accuracy of 95.42% for the incremental pulse scheme.
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Affiliation(s)
- Juri Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Yongjin Park
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Jung-Kyu Lee
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
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Wu Y, Wang Q, Wang Z, Wang X, Ayyagari B, Krishnan S, Chudzik M, Lu WD. Bulk-Switching Memristor-Based Compute-In-Memory Module for Deep Neural Network Training. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2305465. [PMID: 37747134 DOI: 10.1002/adma.202305465] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/12/2023] [Indexed: 09/26/2023]
Abstract
The constant drive to achieve higher performance in deep neural networks (DNNs) has led to the proliferation of very large models. Model training, however, requires intensive computation time and energy. Memristor-based compute-in-memory (CIM) modules can perform vector-matrix multiplication (VMM) in place and in parallel, and have shown great promises in DNN inference applications. However, CIM-based model training faces challenges due to non-linear weight updates, device variations, and low-precision. In this work, a mixed-precision training scheme is experimentally implemented to mitigate these effects using a bulk-switching memristor-based CIM module. Low-precision CIM modules are used to accelerate the expensive VMM operations, with high-precision weight updates accumulated in digital units. Memristor devices are only changed when the accumulated weight update value exceeds a pre-defined threshold. The proposed scheme is implemented with a system-onchip of fully integrated analog CIM modules and digital sub-systems, showing fast convergence of LeNet training to 97.73%. The efficacy of training larger models is evaluated using realistic hardware parameters and verifies that CIM modules can enable efficient mix-precision DNN training with accuracy comparable to full-precision software-trained models. Additionally, models trained on chip are inherently robust to hardware variations, allowing direct mapping to CIM inference chips without additional re-training.
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Affiliation(s)
- Yuting Wu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Qiwen Wang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ziyu Wang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Xinxin Wang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | | | | | - Wei D Lu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
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7
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Wu Z, Li Z, Lin X, Shan X, Chen G, Yang C, Zhao X, Sun Z, Hu K, Wang F, Ren T, Song Z, Zhang K. Diverse long-term potentiation and depression based on multilevel LiSiO xmemristor for neuromorphic computing. NANOTECHNOLOGY 2023; 34:475201. [PMID: 37586343 DOI: 10.1088/1361-6528/acf0c8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 08/15/2023] [Indexed: 08/18/2023]
Abstract
Memristor-based neuromorphic computing is expected to overcome the bottleneck of von Neumann architecture. An artificial synaptic device with continuous conductance variation is essential for implementing bioinspired neuromorphic systems. In this work, a memristor based on Pt/LiSiOx/TiN structure is developed to emulate an artificial synapse, which shows non-volatile multilevel resistance state memory behavior. Moreover, the high nonlinearity caused by abrupt changes in the set process is optimized by adjusting the initial resistance. 100 levels of continuously modulated conductance states are achieved and the nonlinearity factors are reduced to 1.31. The significant improvement is attributed to the decrease in the Schottky barrier height and the evolution of the conductive filaments. Finally, due to the improved linearity of the long-term potentiation/long-term depression behaviors in LiSiOxmemristor, a robust recognition rate (∼94.58%) is achieved for pattern recognition with the modified National Institute of Standards and Technology handwriting database. The Pt/LiSiOx/TiN memristor shows significant potential in high-performance multilevel data storage and neuromorphic computing systems.
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Affiliation(s)
- Zeyu Wu
- School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China
| | - Zewen Li
- School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China
| | - Xin Lin
- School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China
| | - Xin Shan
- School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China
| | - Gang Chen
- School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China
| | - Chen Yang
- School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China
| | - Xuanyu Zhao
- School of Microelectronics, Fudan University, Shanghai 200433, People's Republic of China
| | - Zheng Sun
- School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China
| | - Kai Hu
- School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China
| | - Fang Wang
- School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China
| | - Tianling Ren
- Beijing National Research Center for Information Science and Technology, Institute of Microelectronics, Tsinghua University, Beijing 100084, People's Republic of China
| | - Zhitang Song
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, People's Republic of China
| | - Kailiang Zhang
- School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China
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8
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Ju D, Kim S, Kim S. Artificial Synapse Emulated by Indium Tin Oxide/SiN/TaN Resistive Switching Device for Neuromorphic System. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2477. [PMID: 37686985 PMCID: PMC10490079 DOI: 10.3390/nano13172477] [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: 07/26/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023]
Abstract
In this paper, we fabricate an ITO/SiN/TaN memristor device and analyze its electrical characteristics for a neuromorphic system. The device structure and chemical properties are investigated using transmission electron microscopy and X-ray photoelectron spectroscopy. Uniform bipolar switching is achieved through DC sweep under a compliance current of 5 mA. Also, the analog reset phenomenon is observed by modulating the reset voltage for long-term memory. Additionally, short-term memory characteristics are obtained by controlling the strength of the pulse response. Finally, bio-inspired synaptic characteristics are emulated using Hebbian learning rules such as spike-rate-dependent plasticity (SRDP) and spike-timing-dependent plasticity (STDP). As a result, we believe that the coexistence of short-term and long-term memories in the ITO/SiN/TaN device can provide flexibility in device design in future neuromorphic applications.
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Affiliation(s)
| | | | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea
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9
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Hwang Y, Park B, Hwang S, Choi SW, Kim HS, Kim AR, Choi JW, Yoon J, Kwon JD, Kim Y. A Bioinspired Ultra Flexible Artificial van der Waals 2D-MoS 2 Channel/LiSiO x Solid Electrolyte Synapse Arrays via Laser-Lift Off Process for Wearable Adaptive Neuromorphic Computing. SMALL METHODS 2023:e2201719. [PMID: 36960927 DOI: 10.1002/smtd.202201719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Wearable electronic devices with next-generation biocompatible, mechanical, ultraflexible, and portable sensors are a fast-growing technology. Hardware systems enabling artificial neural networks while consuming low power and processing massive in situ personal data are essential for adaptive wearable neuromorphic edging computing. Herein, the development of an ultraflexible artificial-synaptic array device with concrete-mechanical cyclic endurance consisting of a novel heterostructure with an all-solid-state 2D MoS2 channel and LiSiOx (lithium silicate) is demonstrated. Enabled by the sequential fabrication process of all layers, by excluding the transfer process, artificial van der Waals devices combined with the 2D-MoS2 channel and LiSiOx solid electrolyte exhibit excellent neuromorphic synaptic characteristics with a nonlinearity of 0.55 and asymmetry ratio of 0.22. Based on the excellent flexibility of colorless polyimide substrates and thin-layered structures, the fabricated flexible neuromorphic synaptic devices exhibit superior long-term potentiation and long-term depression cyclic endurance performance, even when bent over 700 times or on curved surfaces with a diameter of 10 mm. Thus, a high classification accuracy of 95% is achieved without any noticeable performance degradation in the Modified National Institute of Standards and Technology. These results are promising for the development of personalized wearable artificial neural systems in the future.
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Affiliation(s)
- Yunjeong Hwang
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Byeongjin Park
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Seungkwon Hwang
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Soo-Won Choi
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Han Seul Kim
- Department of Advanced Materials Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Ah Ra Kim
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Jin Woo Choi
- Department of Data Information and Physics, Kongju National University, 56 Gongjudaehak-ro, Gongju, Chungcheongnam-do, 32588, Republic ofKorea
| | - Jongwon Yoon
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Jung-Dae Kwon
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Yonghun Kim
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
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10
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Baek JH, Kwak KJ, Kim SJ, Kim J, Kim JY, Im IH, Lee S, Kang K, Jang HW. Two-Terminal Lithium-Mediated Artificial Synapses with Enhanced Weight Modulation for Feasible Hardware Neural Networks. NANO-MICRO LETTERS 2023; 15:69. [PMID: 36943534 PMCID: PMC10030746 DOI: 10.1007/s40820-023-01035-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 02/01/2023] [Indexed: 05/25/2023]
Abstract
Recently, artificial synapses involving an electrochemical reaction of Li-ion have been attributed to have remarkable synaptic properties. Three-terminal synaptic transistors utilizing Li-ion intercalation exhibits reliable synaptic characteristics by exploiting the advantage of non-distributed weight updates owing to stable ion migrations. However, the three-terminal configurations with large and complex structures impede the crossbar array implementation required for hardware neuromorphic systems. Meanwhile, achieving adequate synaptic performances through effective Li-ion intercalation in vertical two-terminal synaptic devices for array integration remains challenging. Here, two-terminal Au/LixCoO2/Pt artificial synapses are proposed with the potential for practical implementation of hardware neural networks. The Au/LixCoO2/Pt devices demonstrated extraordinary neuromorphic behaviors based on a progressive dearth of Li in LixCoO2 films. The intercalation and deintercalation of Li-ion inside the films are precisely controlled over the weight control spike, resulting in improved weight control functionality. Various types of synaptic plasticity were imitated and assessed in terms of key factors such as nonlinearity, symmetricity, and dynamic range. Notably, the LixCoO2-based neuromorphic system outperformed three-terminal synaptic transistors in simulations of convolutional neural networks and multilayer perceptrons due to the high linearity and low programming error. These impressive performances suggest the vertical two-terminal Au/LixCoO2/Pt artificial synapses as promising candidates for hardware neural networks.
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Affiliation(s)
- Ji Hyun Baek
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Kyung Ju Kwak
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Seung Ju Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jaehyun Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jae Young Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - In Hyuk Im
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sunyoung Lee
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Kisuk Kang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea.
- Advanced Institute of Convergence Technology, Seoul National University, Suwon, 16229, Korea.
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11
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Seok H, Son S, Jathar SB, Lee J, Kim T. Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:3118. [PMID: 36991829 PMCID: PMC10058286 DOI: 10.3390/s23063118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.
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Affiliation(s)
- Hyunho Seok
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Shihoon Son
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Sagar Bhaurao Jathar
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jaewon Lee
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Taesung Kim
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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12
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Jang HY, Kwon O, Nam JH, Kwon JD, Kim Y, Park W, Cho B. Highly Reproducible Heterosynaptic Plasticity Enabled by MoS 2/ZrO 2-x Heterostructure Memtransistor. ACS APPLIED MATERIALS & INTERFACES 2022; 14:52173-52181. [PMID: 36368778 DOI: 10.1021/acsami.2c15497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Electrically tunable resistive switching of a polycrystalline MoS2-based memtransistor has attracted a great deal of attention as an essential synaptic component of neuromorphic circuitry because its switching characteristics from the field-induced migration of sulfur defects in the MoS2 grain boundaries can realize multilevel conductance tunability and heterosynaptic functionality. However, reproducible switching properties in the memtransistor are usually disturbed by the considerable difficulty in controlling the concentration and distribution of the intrinsically existing sulfur defects. Herein, we demonstrate reliable heterosynaptic characteristics using a memtransistor device with a MoS2/ZrO2-x heterostructure. Compared to the control device with the MoS2 semiconducting channel, the Schottky barrier height was more effectively modulated by the insertion of the insulating ZrO2-x layer below the MoS2, confirmed by an ultraviolet photoelectron spectroscopy analysis and the corresponding energy-band structures. The MoS2/ZrO2-x memtransistor accomplishes dual-terminal (drain and gate electrode) stimulated multilevel conductance owing to the tunable resistive switching behavior under varying gate voltages. Furthermore, the memtransistor exhibits long-term potentiation/depression endurance cycling over 7000 pulses and stable pulse cycling behavior by the pulse stimulus from different terminal regions. The promising candidate as an essential synaptic component of the MoS2/ZrO2-x memtransistors for neuromorphic systems results from the high recognition accuracy (∼92%) of the deep neural network simulation test, based on the training and inference of handwritten numbers (0-9). The simple memtransistor structure facilitates the implementation of complex neural circuitry.
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Affiliation(s)
- Hye Yeon Jang
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Ojun Kwon
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Jae Hyeon Nam
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Jung-Dae Kwon
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science, 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam 51508, Republic of Korea
| | - Yonghun Kim
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science, 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam 51508, Republic of Korea
| | - Woojin Park
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Byungjin Cho
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
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13
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Xu Z, Tang B, Zhang X, Leong JF, Pan J, Hooda S, Zamburg E, Thean AVY. Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch. LIGHT, SCIENCE & APPLICATIONS 2022; 11:288. [PMID: 36202804 PMCID: PMC9537414 DOI: 10.1038/s41377-022-00976-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 08/15/2022] [Accepted: 08/30/2022] [Indexed: 06/16/2023]
Abstract
Photonic neural network has been sought as an alternative solution to surpass the efficiency and speed bottlenecks of electronic neural network. Despite that the integrated Mach-Zehnder Interferometer (MZI) mesh can perform vector-matrix multiplication in photonic neural network, a programmable in-situ nonlinear activation function has not been proposed to date, suppressing further advancement of photonic neural network. Here, we demonstrate an efficient in-situ nonlinear accelerator comprising a unique solution-processed two-dimensional (2D) MoS2 Opto-Resistive RAM Switch (ORS), which exhibits tunable nonlinear resistance switching that allow us to introduce nonlinearity to the photonic neuron which overcomes the linear voltage-power relationship of typical photonic components. Our reconfigurable scheme enables implementation of a wide variety of nonlinear responses. Furthermore, we confirm its feasibility and capability for MNIST handwritten digit recognition, achieving a high accuracy of 91.6%. Our accelerator constitutes a major step towards the realization of in-situ photonic neural network and pave the way for the integration of photonic integrated circuits (PIC).
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Affiliation(s)
- Zefeng Xu
- Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, Singapore.
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.
| | - Baoshan Tang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Xiangyu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Jin Feng Leong
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Jieming Pan
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Sonu Hooda
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Evgeny Zamburg
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Aaron Voon-Yew Thean
- Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, Singapore.
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.
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14
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Wang W, Gao S, Wang Y, Li Y, Yue W, Niu H, Yin F, Guo Y, Shen G. Advances in Emerging Photonic Memristive and Memristive-Like Devices. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2105577. [PMID: 35945187 PMCID: PMC9534950 DOI: 10.1002/advs.202105577] [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: 12/02/2021] [Revised: 06/06/2022] [Indexed: 05/19/2023]
Abstract
Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic memristive and memristive-like devices exhibit an attractive future in constructing novel neuromorphic computing and miniaturized bionic electronic system. Recently, the potential of various emerging materials and structures for photonic memristive and memristive-like devices has attracted tremendous research efforts, generating various novel theories, mechanisms, and applications. Limited by the ambiguity of the mechanism and the reliability of the material, the development and commercialization of such devices are still rare and in their infancy. Therefore, a detailed and systematic review of photonic memristive and memristive-like devices is needed to further promote its development. In this review, the resistive switching mechanisms of photonic memristive and memristive-like devices are first elaborated. Then, a systematic investigation of the active materials, which induce a pivotal influence in the overall performance of photonic memristive and memristive-like devices, is highlighted and evaluated in various indicators. Finally, the recent advanced applications are summarized and discussed. In a word, it is believed that this review provides an extensive impact on many fields of photonic memristive and memristive-like devices, and lay a foundation for academic research and commercial applications.
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Affiliation(s)
- Wenxiao Wang
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Song Gao
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Yaqi Wang
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Yang Li
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Wenjing Yue
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Hongsen Niu
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Feifei Yin
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Yunjian Guo
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Guozhen Shen
- School of Integrated Circuits and ElectronicsBeijing Institute of TechnologyBeijing100081China
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15
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Liu Q, Gao S, Xu L, Yue W, Zhang C, Kan H, Li Y, Shen G. Nanostructured perovskites for nonvolatile memory devices. Chem Soc Rev 2022; 51:3341-3379. [PMID: 35293907 DOI: 10.1039/d1cs00886b] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Perovskite materials have driven tremendous advances in constructing electronic devices owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties, flexible dimensionality engineering, and so on. Particularly, emerging nonvolatile memory devices (eNVMs) based on perovskites give birth to numerous traditional paradigm terminators in the fields of storage and computation. Despite significant exploration efforts being devoted to perovskite-based high-density storage and neuromorphic electronic devices, research studies on materials' dimensionality that has dominant effects on perovskite electronics' performances are paid little attention; therefore, a review from the point of view of structural morphologies of perovskites is essential for constructing perovskite-based devices. Here, recent advances of perovskite-based eNVMs (memristors and field-effect-transistors) are reviewed in terms of the dimensionality of perovskite materials and their potentialities in storage or neuromorphic computing. The corresponding material preparation methods, device structures, working mechanisms, and unique features are showcased and evaluated in detail. Furthermore, a broad spectrum of advanced technologies (e.g., hardware-based neural networks, in-sensor computing, logic operation, physical unclonable functions, and true random number generator), which are successfully achieved for perovskite-based electronics, are investigated. It is obvious that this review will provide benchmarks for designing high-quality perovskite-based electronics for application in storage, neuromorphic computing, artificial intelligence, information security, etc.
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Affiliation(s)
- Qi Liu
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Song Gao
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Lei Xu
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Wenjing Yue
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Chunwei Zhang
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Hao Kan
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
| | - Yang Li
- School of Information Science and Engineering & Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China. .,State Key Laboratory for Superlattices and Microstructures Institute of Semiconductors & Chinese Academy of Sciences and Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing 100083, China.
| | - Guozhen Shen
- State Key Laboratory for Superlattices and Microstructures Institute of Semiconductors & Chinese Academy of Sciences and Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing 100083, China.
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16
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Eshraghian JK, Wang X, Lu WD. Memristor-Based Binarized Spiking Neural Networks: Challenges and Applications. IEEE NANOTECHNOLOGY MAGAZINE 2022. [DOI: 10.1109/mnano.2022.3141443] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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17
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Li L, Hu L, Liu K, Chang KC, Zhang R, Lin X, Zhang S, Huang P, Liu HJ, Kuo TP. Bifunctional homologous alkali-metal artificial synapse with regenerative ability and mechanism imitation of voltage-gated ion channels. MATERIALS HORIZONS 2021; 8:3072-3081. [PMID: 34724525 DOI: 10.1039/d1mh01012c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
As a key component responsible for information processing in the brain, the development of a bionic synapse possessing digital and analog bifunctionality is vital for the hardware implementation of a neuro-system. Here, inspired by the key role of sodium and potassium in synaptic transmission, the alkali metal element lithium (Li) belonging to the same family is adopted in designing a bifunctional artificial synapse. The incorporation of Li endows the electronic devices with versatile synaptic functions. An artificial neural network based on experimental data exhibits a high performance approaching near-ideal accuracy. In addition, the regenerative ability allows synaptic functional recovery through low-frequency stimuli to be emulated, facilitating the prevention of permanent damage due to intensive neural activities and ensuring the long-term stability of the entire neural system. What is more striking for an Li-based bionic synapse is that it can not only emulate a biological synapse at a behavioral level but realize mechanism emulation based on artificial voltage-gated "ion channels". Concurrent digital and analog features lead to versatile synaptic functions in Li-doped artificial synapses, which operate in a mode similar to the human brain with its two hemispheres excelling at processing imaginative and analytical information, respectively.
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Affiliation(s)
- Lei Li
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Luodan Hu
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Kai Liu
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Kuan-Chang Chang
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Rui Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Xinnan Lin
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Shengdong Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Pei Huang
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Heng-Jui Liu
- Department of Materials Science and Engineering, National Chung Hsing University, Taichung 40227, Taiwan
| | - Tzu-Peng Kuo
- Department of Physics, National Sun Yat-sen University, Kaohsiung 804, Taiwan
- Institute of Materials and Optoelectronics, National Sun Yat-sen University, Kaohsiung 804, Taiwan
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18
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Yan Y, Li JC, Chen YT, Wang XY, Cai GR, Park HW, Kim JH, Zhao JS, Hwang CS. Area-Type Electronic Bipolar Switching Al/TiO 1.7/TiO 2/Al Memory with Linear Potentiation and Depression Characteristics. ACS APPLIED MATERIALS & INTERFACES 2021; 13:39561-39572. [PMID: 34378371 DOI: 10.1021/acsami.1c09436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In electronic bipolar resistive switching (eBRS), the electron trapping and detrapping at the defect sites within the switching layer, such as the highly defective TiO1.7 in this study, constitute the switching mechanism. It is an appealing candidate solution to the nonuniformity issue of resistive switching memory. However, TiO1.7-based eBRS has suffered from a lack of endurance and retention. In this study, a 7 nm-thick stoichiometric TiO2 layer is interposed between an Al bottom electrode and a 50 nm-thick TiO1.7 layer, which is in contact with an Al top electrode. Despite the minimal structural modification, improvements in the electrical performance were substantial. The off-to-on state resistance ratio of 20 and the resistance values could be retained up to 30 000 direct current sweep cycles and 106 alternating current pulse switching cycles. Data retention also significantly improves. Moreover, the device is electroforming-free and shows fully area-type switching characteristics. Such notable improvements are attributed to the favorable energy band structure of the Al/TiO1.7/TiO2/Al structure. The device shows almost linear potentiation and depression characteristics after the repeated pulse voltage applications, which significantly improves the accuracy of the neural network, the synapses of which are composed of the Al/TiO1.7/TiO2/Al memory cells.
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Affiliation(s)
- Yu Yan
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshuixi Road, Xiqing District, Tianjin 300384, China
| | - Jia Cheng Li
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshuixi Road, Xiqing District, Tianjin 300384, China
| | - Yu Ting Chen
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshuixi Road, Xiqing District, Tianjin 300384, China
| | - Xiang Yu Wang
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshuixi Road, Xiqing District, Tianjin 300384, China
| | - Gang Ri Cai
- Tianjin Key Laboratory of Organic Solar Cells and Photochemical Conversion, School of Chemistry and Chemical Engineering, Tianjin University of Technology, No. 391, Binshuixi Road, Xiqing District, Tianjin 300384, China
| | - Hyeon Woo Park
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehag-dong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Ji Hun Kim
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehag-dong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jin Shi Zhao
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshuixi Road, Xiqing District, Tianjin 300384, China
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehag-dong, Gwanak-gu, Seoul 08826, Republic of Korea
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Electrical Properties and Biological Synaptic Simulation of Ag/MXene/SiO2/Pt RRAM Devices. ELECTRONICS 2020. [DOI: 10.3390/electronics9122098] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Utilizing electronic devices to emulate biological synapses for the construction of artificial neural networks has provided a feasible research approach for the future development of artificial intelligence systems. Until now, different kinds of electronic devices have been proposed in the realization of biological synapse functions. However, the device stability and the power consumption are major challenges for future industrialization applications. Herein, an electronic synapse of MXene/SiO2 structure-based resistive random-access memory (RRAM) devices has been designed and fabricated by taking advantage of the desirable properties of SiO2 and 2D MXene material. The proposed RRAM devices, Ag/MXene/SiO2/Pt, exhibit the resistance switching characteristics where both the volatile and nonvolatile behaviors coexist in a single device. These intriguing features of the Ag/MXene/SiO2/Pt devices make them more applicable for emulating biological synaptic plasticity. Additionally, the conductive mechanisms of the Ag/MXene/SiO2/Pt RRAM devices have been discussed on the basis of our experimental results.
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