1
|
Wang Z, Li M, Yang H, Shao S, Li J, Deng M, Kang K, Fang Y, Wang H, Zhao J. Enhancement-Mode Carbon Nanotube Optoelectronic Synaptic Transistors with Large and Controllable Threshold Voltage Modulation Window for Broadband Flexible Vision Systems. ACS NANO 2024; 18:14298-14311. [PMID: 38787538 DOI: 10.1021/acsnano.4c00166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
The development of large-scale integration of optoelectronic neuromorphic devices with ultralow power consumption and broadband responses is essential for high-performance bionics vision systems. In this work, we developed a strategy to construct large-scale (40 × 30) enhancement-mode carbon nanotube optoelectronic synaptic transistors with ultralow power consumption (33.9 aJ per pulse) and broadband responses (from 365 to 620 nm) using low-work function yttrium (Y)-gate electrodes and the mixture of eco-friendly photosensitive Ag2S quantum dots (QDs) and ionic liquids (ILs)-cross-linking-poly(4-vinylphenol) (PVP) (ILs-c-PVP) as the dielectric layers. Solution-processable carbon nanotube thin-film transistors (TFTs) showed enhancement-mode characteristics with the wide and controllable threshold voltage window (-1 V∼0 V) owing to use of the low-work-function Y-gate electrodes. It is noted that carbon nanotube optoelectronic synaptic transistors exhibited high on/off ratios (>106), small hysteresis and low operating voltage (≤2 V), and enhancement mode even under the illumination of ultraviolet (UV, 365 nm), blue (450 nm), and green (550 nm) to red (620 nm) pulse lights when introducing eco-friendly Ag2S QDs in dielectric layers, demonstrating that they have the strong fault-tolerant ability for the threshold voltage drifts caused by various manufacturing scenarios. Furthermore, some important bionic functions including a high paired pulse facilitation index (PPF index, up to 290%), learning and memory function with the long duration (200 s), and rapid recovery (2 s). Pavlov's dog experiment (retention time up to 20 min) and visual memory forgetting experiments (the duration of high current for 180 s) are also demonstrated. Significantly, the optoelectronic synaptic transistors can be used to simulate the adaptive process of vision in varying light conditions, and we demonstrated the dynamic transition of light adaptation to dark adaptation based on light-induced conditional behavior. This work undoubtedly provides valuable insights for the future development of artificial vision systems.
Collapse
Affiliation(s)
- Zebin Wang
- Institute of Nano Science and Technology, University of Science and Technology of China, No. 166 Ren Ai Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Min Li
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Hongchao Yang
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Shuangshuang Shao
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Jiaqi Li
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Meng Deng
- Institute of Nano Science and Technology, University of Science and Technology of China, No. 166 Ren Ai Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Kaixiang Kang
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Yuxiao Fang
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Hua Wang
- Key Laboratory of Interface Science and Engineering in Advanced Materials of Ministry of Education, Taiyuan University of Technology, NO.79, Yingze West Main Street, Taiyuan, Shanxi Province 030024, P.R. China
| | - Jianwen Zhao
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| |
Collapse
|
2
|
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
|
3
|
Wang WS, Shi ZW, Chen XL, Li Y, Xiao H, Zeng YH, Pi XD, Zhu LQ. Biodegradable Oxide Neuromorphic Transistors for Neuromorphic Computing and Anxiety Disorder Emulation. ACS APPLIED MATERIALS & INTERFACES 2023; 15:47640-47648. [PMID: 37772806 DOI: 10.1021/acsami.3c07671] [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: 09/30/2023]
Abstract
Brain-inspired neuromorphic computing and portable intelligent electronic products have received increasing attention. In the present work, nanocellulose-gated indium tin oxide neuromorphic transistors are fabricated. The device exhibits good electrical performance. Short-term synaptic plasticities were mimicked, including excitatory postsynaptic current, paired-pulse facilitation, and dynamic high-pass synaptic filtering. Interestingly, an effective linear synaptic weight updating strategy was adopted, resulting in an excellent recognition accuracy of ∼92.93% for the Modified National Institute of Standard and Technology database adopting a two-layer multilayer perceptron neural network. Moreover, with unique interfacial protonic coupling, anxiety disorder behavior was conceptually emulated, exhibiting "neurosensitization", "primary and secondary fear", and "fear-adrenaline secretion-exacerbated fear". Finally, the neuromorphic transistors could be dissolved in water, demonstrating potential in "green" electronics. These findings indicate that the proposed oxide neuromorphic transistors would have potential as implantable chips for nerve health diagnosis, neural prostheses, and brain-machine interfaces.
Collapse
Affiliation(s)
- Wei Sheng Wang
- School of Physical Science and Technology, Ningbo University, Ningbo 315211, Zhejiang, P.R. China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, Zhejiang, P.R. China
| | - Zhi Wen Shi
- School of Physical Science and Technology, Ningbo University, Ningbo 315211, Zhejiang, P.R. China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, Zhejiang, P.R. China
| | - Xin Li Chen
- School of Physical Science and Technology, Ningbo University, Ningbo 315211, Zhejiang, P.R. China
| | - Yan Li
- School of Physical Science and Technology, Ningbo University, Ningbo 315211, Zhejiang, P.R. China
| | - Hui Xiao
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, Zhejiang, P.R. China
| | - Yu Heng Zeng
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, Zhejiang, P.R. China
| | - Xiao Dong Pi
- State Key Laboratory of Silicon Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Li Qiang Zhu
- School of Physical Science and Technology, Ningbo University, Ningbo 315211, Zhejiang, P.R. China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, Zhejiang, P.R. China
| |
Collapse
|
4
|
Zhou Y, Zhang P, Li J, Mao X. Inhibitory artificial synapses based on photoelectric co-modulation of graphene/WSe 2van der Waals heterojunctions. NANOTECHNOLOGY 2023; 34:505203. [PMID: 37689056 DOI: 10.1088/1361-6528/acf82d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/08/2023] [Indexed: 09/11/2023]
Abstract
Optical artificial synapses possess several advantages, including high bandwidth, strong interference immunity, and ultra-fast signal transmission, overcoming the limitations of electrically stimulated synapses. Among various functional materials, 2D materials exhibit exceptional optical and electrical properties. By utilizing van der Waals heterostructures formed by these materials through rational design, synaptic devices can mimic the information perception ability of biological systems. This lays the foundation for low-energy artificial vision systems and neuromorphic computing. This study introduces an inhibitory artificial synapse based on photoelectric co-modulation of graphene/WSe2van der Waals heterojunctions. By synergistically applying gate voltage and light pulses, we simulate memory and logic functions observed in the brain's visual cortex. We achieve the construction of inhibitory synapses, enabling properties such as postsynaptic current response, short-term and long-term plasticity, and paired-pulse facilitation. Additionally, we accomplish the inverse recovery of device conductivity through separate gate voltage stimulation. Through bidirectional modulation of the artificial synaptic conductance, we construct an artificial hardware neural network that achieves 92.5% accuracy in recognizing handwritten digital images from the MNIST dataset. The network also has good recognition accuracy for handwritten digital images with different standard deviation Gaussian noise applied and other datasets. Furthermore, we successfully mimic the neural behavior of aversive learning for alcohol withdrawal in alcoholic patients using the device properties. The promising capabilities of artificial synapses constructed through electrical and optical synergistic modulation make them suitable for wearable electronics and artificial vision systems.
Collapse
Affiliation(s)
- Youfa Zhou
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100089, People's Republic of China
| | - Ping Zhang
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Jiaqi Li
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100089, People's Republic of China
| | - Xurui Mao
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100089, People's Republic of China
- College of Materials Science and Opto-electronic Engineering, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| |
Collapse
|
5
|
Huang M, Ali W, Yang L, Huang J, Yao C, Xie Y, Sun R, Zhu C, Tan Y, Liu X, Li S, Li Z, Pan A. Multifunctional Optoelectronic Synapses Based on Arrayed MoS 2 Monolayers Emulating Human Association Memory. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300120. [PMID: 37058134 DOI: 10.1002/advs.202300120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/01/2023] [Indexed: 06/04/2023]
Abstract
Optoelectronic synaptic devices integrating light-perception and signal-storage functions hold great potential in neuromorphic computing for visual information processing, as well as complex brain-like learning, memorizing, and reasoning. Herein, the successful growth of MoS2 monolayer arrays assisted by gold nanorods guided precursor nucleation is demonstrated. Optical, spectral, and morphology characterizations of MoS2 prove that arrayed flakes are homogeneous monolayers, and they are further fabricated as optoelectronic devices showing featured photocurrent loops and stable optical responses. Typical synaptic behaviors of photo-induced short-term potentiation, long-term potentiation, and paired pulse facilitation are recorded under different light stimulations of 450, 532, and 633 nm lasers at various excitation powers. A visual sensing system consisting of 5 × 6 pixels is constructed to simulate the light-sensing image mapped by forgetting curves in real time. Moreover, the system presents the ability of utilizing associated images to restore vague and incomplete memories, which successfully mimics human intelligent behaviors of association memory and logical reasoning. The work emulates the brain-like artificial intelligence using arrayed 2D semiconductors, which paves an avenue to achieve smart retina and complex brain-like system.
Collapse
Affiliation(s)
- Ming Huang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Wajid Ali
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Liuli Yang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Jianhua Huang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Chengdong Yao
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Yunfei Xie
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Ronghuan Sun
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Chenguang Zhu
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Yike Tan
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Xiao Liu
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Shengman Li
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Ziwei Li
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Anlian Pan
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, Hunan Institute of Optoelectronic Integration, College of Materials Science and Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| |
Collapse
|
6
|
Kim JP, Kim SK, Park S, Kuk SH, Kim T, Kim BH, Ahn SH, Cho YH, Jeong Y, Choi SY, Kim S. Dielectric-Engineered High-Speed, Low-Power, Highly Reliable Charge Trap Flash-Based Synaptic Device for Neuromorphic Computing beyond Inference. NANO LETTERS 2023; 23:451-461. [PMID: 36637103 DOI: 10.1021/acs.nanolett.2c03453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The coming of the big-data era brought a need for power-efficient computing that cannot be realized in the Von Neumann architecture. Neuromorphic computing which is motivated by the human brain can greatly reduce power consumption through matrix multiplication, and a device that mimics a human synapse plays an important role. However, many synaptic devices suffer from limited linearity and symmetry without using incremental step pulse programming (ISPP). In this work, we demonstrated a charge-trap flash (CTF)-based synaptic transistor using trap-level engineered Al2O3/Ta2O5/Al2O3 gate stack for successful neuromorphic computing. This novel gate stack provided precise control of the conductance with more than 6 bits. We chose the appropriate bias for highly linear and symmetric modulation of conductance and realized it with very short (25 ns) identical pulses at low voltage, resulting in low power consumption and high reliability. Finally, we achieved high learning accuracy in the training of 60000 MNIST images.
Collapse
Affiliation(s)
- Joon Pyo Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Seong Kwang Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Seohak Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Song-Hyeon Kuk
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Taeyoon Kim
- Korea Institute of Science and Technology (KIST), Seoul02792, Republic of Korea
| | - Bong Ho Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Seong-Hun Ahn
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Yong-Hoon Cho
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - YeonJoo Jeong
- Korea Institute of Science and Technology (KIST), Seoul02792, Republic of Korea
| | - Sung-Yool Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Sanghyeon Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| |
Collapse
|