Article
Materials Science, Multidisciplinary
Eun Seo Jo, You Seung Rim
Summary: We conducted research to create reverse synapse plasticity using metal oxide semiconductor-based field-effect transistors. By adjusting the thickness of the Ga2O3 trapping layers, we examined changes in roughness and density and confirmed the variations and mechanisms of synaptic behaviors in relation to the properties of Ga2O3. The control of charge traps as functions of pulse time, input voltage, and initialization is crucial for achieving optimal device conditions.
MATERIALS TODAY PHYSICS
(2023)
Article
Engineering, Electrical & Electronic
Rongqi Wu, Xiaochi Liu, Zhongwang Wang, Yumei Jing, Yahua Yuan, Kui Tang, Xianfu Dai, Aocheng Qiu, Hemendra N. Jaiswal, Jia Sun, Huamin Li, Jian Sun
Summary: This article demonstrates an ambipolar WSe2 synaptic FET with both electron and hole charge traps, exhibiting balanced excitation and inhibition. It achieves nearly zero asymmetry with low nonlinearity of 0.04 in long-term potentiation and depression (LTP/LTD). The proposed devices have great potential in high-accuracy neuromorphic computing applications.
IEEE ELECTRON DEVICE LETTERS
(2023)
Article
Physics, Applied
Yushan Li, Ruiqiang Tao, Waner He, Cheng Chang, Zhengmiao Zou, Yan Zhang, Dao Wang, Jiali Wang, Zhen Fan, Guofu Zhou, Xubing Lu, Junming Liu
Summary: The proposed transistor-based artificial synapse successfully emulates essential synaptic functions and provides a feasible device structure for future demands of neuromorphic computing.
JOURNAL OF APPLIED PHYSICS
(2021)
Article
Computer Science, Artificial Intelligence
Yeonsu Kang, Jiung Jang, Danyoung Cha, Sungsik Lee
Summary: The study explores weight modulation and charge trapping mechanisms of a synaptic transistor based on a pass-transistor concept, enabling direct voltage output and demonstrating the ability of synaptic pass transistors to mimic neural functions. Results show that SPTs can achieve self-normalization of synaptic weights, exhibit low power consumption at a device level, and sufficient accuracy at an array level, closely resembling biological synapses.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Materials Science, Multidisciplinary
Peng Zhang, Emmanuel Jacques, Laurent Pichon, Olivier Bonnaud
Summary: An organic ambipolar charge trapping non-volatile memory device is proposed based on a specific material double heterojunction structure, which achieves a large memory window and fully-electrical programming and erasing through the combination of two trapping layers.
Article
Chemistry, Physical
Huanhuan Wei, Yao Ni, Lin Sun, Haiyang Yu, Jiangdong Gong, Yi Du, Mingxue Ma, Hong Han, Wentao Xu
Summary: The proposed electro-optical modulation synaptic device can emulate brain-like processing and nervous perception functions using a nanoparticle-based conductive channel. The device exhibits various synaptic functions and mechanical flexibility, making it a promising candidate for neuromorphic and flexible electronics applications.
Article
Engineering, Electrical & Electronic
Hui Li, Yanan Ding, Haiyang Qiu, Yixin Zhu, Chengzhe Han, Guoxia Liu, Fukai Shan
Summary: In this study, a flexible and compatible synaptic transistor based on one-dimensional indium oxide nanofibers was fabricated, and the synaptic behavior was successfully simulated. The transistor showed good biocompatibility and environmental compatibility, and achieved high accuracy in pattern recognition.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2022)
Article
Materials Science, Multidisciplinary
Xianghong Zhang, Enlong Li, Rengjian Yu, Lihua He, Weijie Yu, Huipeng Chen, Tailiang Guo
Summary: This paper introduces a new type of multilevel photosensitive blending semiconductor optoelectronic synaptic transistor (MOST), which achieves 8 stable states and synaptic behaviors under illumination in the wavelength range of 480-800 nm. The optical decoder model based on MOST is successfully fabricated and applied in neural machine translation, significantly simplifying the structure of traditional systems and laying the foundation for breaking the bottleneck of machine translation.
SCIENCE CHINA-MATERIALS
(2022)
Article
Chemistry, Multidisciplinary
Tianshi Zhao, Chun Zhao, Wangying Xu, Yina Liu, Hao Gao, Ivona Z. Mitrovic, Eng Gee Lim, Li Yang, Ce Zhou Zhao
Summary: This paper proposes a photoelectronic transistor that mimics biological synaptic behaviors and demonstrates its typical synaptic behaviors and reliable memory stability through voltage testing. The UV-responsive synaptic properties of the MXenes floating gated transistor and its applications are measured and realized for the first time, showing great potential in bio-imitation vision applications. Through simulation based on an artificial neural network algorithm, the device successfully achieves recognition of handwritten digital images, providing a highly feasible solution for applying artificial synaptic devices to hardware neuromorphic networks.
ADVANCED FUNCTIONAL MATERIALS
(2021)
Article
Chemistry, Multidisciplinary
Wei Wang, Weijun Wang, You Meng, Quan Quan, Zhengxun Lai, Dengji Li, Pengshan Xie, SenPo Yip, Xiaolin Kang, Xiuming Bu, Dong Chen, Chuntai Liu, Johnny C. Ho
Summary: A gate-tunable and anti-ambipolar phototransistor based on 1D GaAsSb nanowire/2D MoS2 nanoflake mixed-dimensional van der Waals heterojunctions is reported. The device shows asymmetric control over anti-ambipolar transfer characteristics, enabling electronic functions and gate-tunability of the photoresponse. It exhibits high-performance photodetection and photovoltaic response.
Article
Engineering, Electrical & Electronic
Chen Wang, Hao Liu, Lin Chen, Hao Zhu, Li Ji, Qing-Qing Sun, David Wei Zhang
Summary: This study introduces artificial synaptic transistors based on 2D TMDCs, fabricated on atomic layer deposited MoS2 thin film with integrated high-k dielectric gate stack. These synapse cells can simulate basic synaptic functions, showing ultralow power consumption and simulation results of multi-synapse networks.
IEEE ELECTRON DEVICE LETTERS
(2021)
Article
Engineering, Electrical & Electronic
Shuqiong Lan, Xiaoyan Wang, Rengjian Yu, Changjie Zhou, Minshuai Wang, Xiaomei Cai
Summary: Traditional Von-Neumann computers cannot meet the demands of storing and processing large amounts of information in the era of artificial intelligence. A novel synaptic device based on a hybrid trapping layer is proposed, exhibiting larger memory window and stronger trapping capability. By simulating typical synaptic properties and an artificial neural network, the device demonstrates high performance.
IEEE ELECTRON DEVICE LETTERS
(2022)
Article
Materials Science, Ceramics
Qiu Haiyang, Miao Guangtan, Li Hui, Luan Qi, Liu Guoxia, Shan Fukai
Summary: As the basic unit of neuromorphic computing system, electrolyte-gated synaptic transistors (EGSTs) exhibit great potential in various applications. However, most EGSTs suffer from short persistence for long-term plasticity (LTP) and difficulties in adjusting channel conductance. Plasma treatments on the channel surface can enhance the LTP of EGSTs by capturing hydrogen ions at the electrolyte/channel interface. By mimicking biological synaptic functions and utilizing a three-layer artificial neural network, EGSTs achieve high recognition accuracy in handwritten digit recognition.
JOURNAL OF INORGANIC MATERIALS
(2023)
Article
Engineering, Electrical & Electronic
Jianxiong Zou, Qia Zhang, Jin Ai, Ling Kang, Menghan Deng, Jinzhong Zhang, Wenwu Li, Jian Zhang
Summary: This study proposes and fabricates a novel organic synaptic transistor (OST) based on a hybrid gate dielectric, which exhibits a large hysteresis window and good memory performance. The OST can mimic typical synaptic functions and has potential applications in neuromorphic systems and artificial neural networks.
IEEE ELECTRON DEVICE LETTERS
(2023)
Article
Nanoscience & Nanotechnology
P. Monalisha, Anil P. S. Kumar, Xiao Renshaw Wang, S. N. Piramanayagam
Summary: This study demonstrated a synaptic transistor based on a metallic cobalt thin film, capable of emulating key synaptic functions and achieving gate-controlled, multilevel conductance states and cognitive behaviors, providing insights for neuromorphic computing.
ACS APPLIED MATERIALS & INTERFACES
(2022)
Review
Chemistry, Multidisciplinary
Guangxiong Duan, Shenming Huang, Zihao Feng, Peng Xie, Fan Zhang, Ye Zhou, Su-Ting Han
Summary: In recent years, a variety of hardware-based artificial sensory systems have attracted significant research interest in advanced artificial intelligence systems. This review focuses on the development of field-effect transistor (FET)-based gas sensory devices, discussing their mechanisms, gas recognition materials, strategies for improving sensing performance, and integration into artificial olfactory systems. The potential of FET-based sensory devices for next-generation intelligent sensory systems in fields such as environmental monitoring, health care, and military industries is also discussed.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Review
Chemistry, Multidisciplinary
Kui Zhou, Gang Shang, Hsiao-Hsuan Hsu, Su-Ting Han, Vellaisamy A. L. Roy, Ye Zhou
Summary: This review provides an overview of recent advances in the synthesis of 2D metal oxides and their electronic applications. It discusses the tunable physical properties of 2D metal oxides related to structure, crystallinity, defects, and thickness. It also introduces advanced synthesis methods and various roles of 2D metal oxides in applications such as transistors, photodetectors, and solar cells.
ADVANCED MATERIALS
(2023)
Review
Chemistry, Multidisciplinary
Guohua Zhang, Jingrun Qin, Yue Zhang, Guodong Gong, Zi-Yu Xiong, Xiangyu Ma, Ziyu Lv, Ye Zhou, Su-Ting Han
Summary: The booming development of artificial intelligence (AI) requires faster physical processing units and more efficient algorithms. Reservoir computing (RC) has emerged as an alternative brain-inspired framework for fast learning and low training cost, and memristor-based physical RC systems have gained increasing popularity. This review summarizes the architectures, materials, and applications of memristor-implemented RC systems, explores the dynamic memory behaviors of memristors based on various material systems, and surveys recent advances in the application of memristor-based physical RC systems.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Review
Chemistry, Multidisciplinary
Gang Liu, Ziyu Lv, Saima Batool, Ming-Zheng Li, Pengfei Zhao, Liangchao Guo, Yan Wang, Ye Zhou, Su-Ting Han
Summary: With the rapid growth of artificial intelligence, big data, the Internet of Things, and 5G/6G technologies, there is a growing need for humans to pursue life and manage personal or family health. The use of micro biosensing devices is crucial in bridging the gap between technology and personalized medicine.
Article
Chemistry, Physical
Cheng Zhang, Yang Li, Fei Yu, Guan Wang, Kuaibing Wang, Chunlan Ma, Xinbo Yang, Ye Zhou, Qichun Zhang
Summary: 2D ribbon-structured hydrogen-bonded organic frameworks (Nano-HOFs) embedded with transition metal nanoparticles are used as reliable memristive materials to mimic synaptic behaviors. The HOFs@Au-based memristor shows gradient electrical conductances and stable synaptic functions, making it suitable for neuromorphic computing and intelligent cognition applications.
Article
Physics, Applied
Yongbiao Zhai, Peng Xie, Jiahui Hu, Xue Chen, Zihao Feng, Ziyu Lv, Guanglong Ding, Kui Zhou, Ye Zhou, Su-Ting Han
Summary: To meet the requirements of data-intensive computing in the data-explosive era, researchers have extensively investigated brain-inspired neuromorphic computing for the past decade. However, challenges in integrating synaptic and neuronal devices in a single chip due to incompatible preparation processes have limited energy efficiency and scalability. Therefore, the development of a reconfigurable device with synaptic and neuronal functions in a single chip using the same materials and structures is highly desired. In this study, a reconfigurable hardware platform based on the polarization effect of 2D alpha-In2Se3 was designed, which can switch between emulating synapse and mimicking neuron. The application of this proof-of-concept device on a spiking neural network demonstrated its powerful learning ability and efficiency.
APPLIED PHYSICS REVIEWS
(2023)
Review
Chemistry, Multidisciplinary
Shirui Zhu, Tao Xie, Ziyu Lv, Yan-Bing Leng, Yu-Qi Zhang, Runze Xu, Jingrun Qin, Ye Zhou, Vellaisamy A. L. Roy, Su-Ting Han
Summary: The development of artificial intelligence poses challenges to conventional machine vision due to its high latency and inefficient power consumption. By gaining insights into the function of each part of the visual pathway and using neuromorphic devices and circuits, machine vision systems can be improved in terms of robustness and energy efficiency.
ADVANCED MATERIALS
(2023)
Article
Chemistry, Physical
Kui Zhou, Ziqi Jia, Yao Zhou, Guanglong Ding, Xin-Qi Ma, Wenbiao Niu, Su-Ting Han, Jiyu Zhao, Ye Zhou
Summary: Neuromorphic computing has drawn extensive research interest in the development of novel neuromorphic memory devices, like memristors and bioinspired artificial synaptic devices, to overcome the limitations of conventional von Neumann architectures. Covalent organic frameworks (COFs), as crystalline porous polymers, offer customizable structures and pores for interactions with various entities such as photons, excitons, electrons, holes, ions, spins, and molecules, making them promising materials for neuromorphic electronics. This Perspective article focuses on the molecular design, thin-film processing, and neuromorphic applications of COF materials for neuromorphic memory devices, providing future directions and potential applications for COF-based neuromorphic electronics.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2023)
Editorial Material
Materials Science, Multidisciplinary
Ye Zhou, Su-Ting Han
SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS
(2023)
Article
Chemistry, Physical
Guanglong Ding, Su-Ting Han, Chi-Ching Kuo, Vellaisamy A. L. Roy, Ye Zhou
Summary: Porphyrin-based metal-organic frameworks (PP-MOFs) have attracted increasing attention in the field of neuromorphic electronics due to their superior optoelectronic characteristics, the ability to form 2D layered structure, and customizability. However, the related application research is in the initial stage, demanding a timely summary and guidance. This article highlights the PP-MOFs fabrication shift, from powder synthesis to high-quality film preparation, and introduces the advances and challenges in neuromorphic electronics, aiming to attract experts from various areas and promote the application of PP-MOFs.
Article
Chemistry, Multidisciplinary
Hua-Xin Li, Qing-Xiu Li, Fu-Zhi Li, Jia-Peng Liu, Guo-Dong Gong, Yu-Qi Zhang, Yan-Bing Leng, Tao Sun, Ye Zhou, Su-Ting Han
Summary: Memristor is a promising technology for future computing systems due to its low-power, high density, and scalability. However, there are still challenges to overcome, such as nonideal device characteristics. In this study, a high-performance memristor based on ITO/Ni single-atoms (NiSAs/N-C)/PVP/Au structure was developed, with improved switching speed and retention capability through the modulation of defect distribution and trapping level.
ADVANCED MATERIALS
(2023)
Review
Chemistry, Multidisciplinary
Guanglong Ding, JiYu Zhao, Kui Zhou, Qi Zheng, Su-Ting Han, Xiaojun Peng, Ye Zhou
Summary: Porous crystalline materials have been widely studied and applied in memory and neuromorphic computing systems. The preparation of high-quality films is crucial for achieving high device performance.
CHEMICAL SOCIETY REVIEWS
(2023)
Article
Engineering, Electrical & Electronic
Wei Xu, Zhijuan Li, Zetao Fang, Bo Wang, Linze Hong, Gai Yang, Su-Ting Han, Xiaojin Zhao, Xiaoyi Wang
Summary: This article presents a CMOS-MEMS monolithic integrated thermal flow sensor system with high accuracy, low power consumption, and low noise. The sensor has high sensitivity, wide measurement range, and good linearity, which has been experimentally verified to be feasible.
IEEE JOURNAL OF SOLID-STATE CIRCUITS
(2023)
