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Sun S, Zhang M, Bian J, Xu T, Su J. In 2O 3/ZnO heterojunction thin film transistor for high recognition accuracy neuromorphic computing and optoelectronic artificial synapses. NANOTECHNOLOGY 2024; 35:365602. [PMID: 38861958 DOI: 10.1088/1361-6528/ad5685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/11/2024] [Indexed: 06/13/2024]
Abstract
Solid electrolyte-gated transistors exhibit improved chemical stability and can fulfill the requirements of microelectronic packaging. Typically, metal oxide semiconductors are employed as channel materials. However, the extrinsic electron transport properties of these oxides, which are often prone to defects, pose limitations on the overall electrical performance. Achieving excellent repeatability and stability of transistors through the solution process remains a challenging task. In this study, we propose the utilization of a solution-based method to fabricate an In2O3/ZnO heterojunction structure, enabling the development of efficient multifunctional optoelectronic devices. The heterojunction's upper and lower interfaces induce energy band bending, resulting in the accumulation of a large number of electrons and a significant enhancement in transistor mobility. To mimic synaptic plasticity responses to electrical and optical stimuli, we utilize Li+-doped high-k ZrOxthin films as a solid electrolyte in the device. Notably, the heterojunction transistor-based convolutional neural network achieves a high accuracy rate of 93% in recognizing handwritten digits. Moreover, our research involves the simulation of a typical sensory neuron, specifically a nociceptor, within our synaptic transistor. This research offers a novel avenue for the advancement of cost-effective three-terminal thin-film transistors tailored for neuromorphic applications.
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Affiliation(s)
- Shangheng Sun
- School of Physics Science, Qingdao University, Qingdao 266071, People's Republic of China
| | - Minghao Zhang
- School of Physics Science, Qingdao University, Qingdao 266071, People's Republic of China
| | - Jing Bian
- School of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People's Republic of China
| | - Ting Xu
- School of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People's Republic of China
| | - Jie Su
- School of Physics Science, Qingdao University, Qingdao 266071, People's Republic of China
- School of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People's Republic of China
- National Laboratory of Solid State Microstructures, Physics Department, Nanjing University, Nanjing 210093, People's Republic of China
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Mallik S, Tsuruoka T, Tsuchiya T, Terabe K. Effects of Mg Doping to a LiCoO 2 Channel on the Synaptic Plasticity of Li Ion-Gated Transistors. ACS APPLIED MATERIALS & INTERFACES 2023; 15:47184-47195. [PMID: 37768881 DOI: 10.1021/acsami.3c07833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
Artificial synapses with ideal functionalities are essential in hardware neural networks to allow for energy-efficient analog computing. However, the realization of linear and symmetric weight updates in real synaptic devices has proven challenging and ultimately limits the online training capabilities of neural network systems. Herein, we investigate the effect of Mg doping on a LiCoO2 (LCO) channel in a Li ion-gated synaptic transistor, so as to improve long-term and short-term plasticity. Two transistor structures, based on a lithium phosphorus oxynitride electrolyte, were examined by using undoped LCO and Mg-doped LCO as the channel material between the source and drain electrodes. It was found that Mg doping increased the initial channel conductance by 3 orders of magnitude, which is probably due to the substitution of Co3+ by Mg2+ and the compensation of hole creation. It was further found that the doped channel transistor showed good retention characteristics and better linearity of long-term potentiation and depression when voltage pulses were applied to the gate electrode. The improved retention and linearity are attributed to an extended range of the insulator-to-conductor transition by Mg doping and Li-ion extraction/insertion cooperated in the LCO channel. Using the obtained synaptic weight update, artificial neural network simulations demonstrated that the doped channel transistor shows an image recognition accuracy of ∼80% for handwritten digits, which is higher than ∼65% exhibited by the undoped channel transistor. Mg doping also improved short-term plasticity such as paired-pulse facilitation/depression and Hebbian spike timing-dependent plasticity. These results indicate that elemental doping to the channel of Li ion-gated synaptic transistors could be a useful procedure for realizing robust neuromorphic systems based on analog computing.
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Affiliation(s)
- Samapika Mallik
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science, Namiki 1-1, Tsukuba, 305-0044, Japan
| | - Tohru Tsuruoka
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science, Namiki 1-1, Tsukuba, 305-0044, Japan
| | - Takashi Tsuchiya
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science, Namiki 1-1, Tsukuba, 305-0044, Japan
| | - Kazuya Terabe
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science, Namiki 1-1, Tsukuba, 305-0044, Japan
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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.
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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
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