Article
Materials Science, Multidisciplinary
T. Guo, K. Pan, B. Sun, L. Wei, Y. Yan, Y. N. Zhou, Y. A. Wu
Summary: The development of the first adjustable LIF neuron using novel memristor-coupled capacitors was achieved, along with the use of genetic algorithm to detach the entanglement of capacitive and memristive effects. This method can be generalized to other entangled physical behaviors, facilitating the development of novel circuits.
MATERIALS TODAY ADVANCES
(2021)
Review
Materials Science, Multidisciplinary
Hui Chen, Huilin Li, Ting Ma, Shuangshuang Han, Qiuping Zhao
Summary: As the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse, neurons, and intelligent behaviors, and present some challenges and prospects related to neuromorphic devices.
SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS
(2023)
Article
Physics, Multidisciplinary
Wen Xin-Yu, Wang Ya-Sai, He Yu-Hui, Miao Xiang-Shui
Summary: With the rapid development of deep learning, the current need for hardware computing power has increased. Traditional CMOS integration is unable to meet the growing demands, leading to the utilization of new device structures such as memristors. This article reviews the device structure and physical mechanism of mainstream memristors, explores their performance characteristics and recent research progress in realizing artificial neurons and synapses. It also reviews the structural forms and applications of passive and active memristive arrays in neuromorphic computing, and summarizes the challenges and future development prospects in this field.
ACTA PHYSICA SINICA
(2022)
Article
Chemistry, Physical
Li Zhang, Zhenhua Tang, Junlin Fang, Xiujuan Jiang, Yan-Ping Jiang, Qi-Jun Sun, Jing-Min Fan, Xin-Gui Tang, Gaokuo Zhong
Summary: Artificial neural network-based computing has the potential to overcome the limitations of conventional computers and has a wide range of applications. By using NiO/Cu2O memristors to emulate biological synapses, the recognition accuracy of an artificial neural network based on synaptic weight modulation reached up to 96.84% on average, demonstrating the potential of artificial synapses in artificial intelligence systems.
APPLIED SURFACE SCIENCE
(2022)
Article
Chemistry, Multidisciplinary
Lingli Liu, Putu Andhita Dananjaya, Calvin Ching Ian Ang, Eng Kang Koh, Gerard Joseph Lim, Han Yin Poh, Mun Yin Chee, Calvin Xiu Xian Lee, Wen Siang Lew
Summary: A three-terminal memristor based on oxygen ion migration is developed to function as both a synapse and a neuron. It exhibits short-term plasticity and learning capability, and can emulate the leaky-integrate-and-fire neuronal model by leveraging short-term dynamics. The proposed 3TM offers more process compatibility for integrating synaptic and neuronal components in the hardware implementation of a spiking neural network.
Article
Materials Science, Ceramics
Tian Yu, Zhu Xiaojian, Sun Cui, Ye Xiaoyu, Liu Huiyuan, Li Runwei
Summary: An intrinsically stretchable threshold-switching memristor was developed using a silver nanowire-polyurethane composite as the dielectric layer and liquid metal as the electrodes. The device can emulate the computing functions of biological neurons and exhibits excellent mechanical flexibility and stability.
JOURNAL OF INORGANIC MATERIALS
(2023)
Article
Computer Science, Artificial Intelligence
Mohammad Saeed Feali
Summary: The adaptive response to timely constant stimuli is a common feature of real neurons. Neuron models that adapt consume less power and bandwidth compared to those that do not, especially for encoding time-varying signals. Memristors are efficient in mimicking neuron behavior, with short-term adaptation achievable through their volatile switching property, leading to reduced power dissipation.
Article
Nanoscience & Nanotechnology
Caidie Cheng, Yanghao Wang, Liying Xu, Keqin Liu, Bingjie Dang, Yingming Lu, Xiaoqin Yan, Ru Huang, Yuchao Yang
Summary: Neuromorphic systems usually focus on artificial synapses and neurons, neglecting the crucial role of astrocyte cells. This study demonstrates an astrocyte memristor with improved performance and linearity recovery characteristics, showing great potential for neuromorphic computing.
ADVANCED ELECTRONIC MATERIALS
(2022)
Review
Nanoscience & Nanotechnology
Guangdong Zhou, Zhongrui Wang, Bai Sun, Feichi Zhou, Linfeng Sun, Hongbin Zhao, Xiaofang Hu, Xiaoyan Peng, Jia Yan, Huamin Wang, Wenhua Wang, Jie Li, Bingtao Yan, Dalong Kuang, Yuchen Wang, Lidan Wang, Shukai Duan
Summary: This article systematically surveys four types of nonvolatile memristors and four types of volatile memristors, exploring their switching mechanisms and electrical properties for different computing applications. The volatile memristors can emulate synaptic and neural dynamics, while nonvolatile memristors can implement key convolutional operations.
ADVANCED ELECTRONIC MATERIALS
(2022)
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
Chemistry, Multidisciplinary
Yiqun Liu, Yonghuang Wu, Haojie Han, Yue Wang, Ruixuan Peng, Kai Liu, Di Yi, Ce-Wen Nan, Jing Ma
Summary: This research proposes an optoelectronic synaptic device with a simple structure but multifunctional capabilities, and demonstrates its potential applications in visual perception, processing, and memory. The device can emulate both short-term and long-term memory and simulate light adaptive behavior through optical-electrical cooperative modulation.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Chemistry, Physical
Osung Kwon, Yewon Lee, Myounggon Kang, Sungjun Kim
Summary: In this paper, the memory characteristics of Ag/AlN/TiN devices for neuromorphic systems were investigated. The thickness and components of the device stack were verified using TEM and EDS. The long-term memory (LTM) and short-term memory (STM) characteristics were determined by compliance current (CC), with LTM characteristics observed at high CC and STM characteristics observed at low CC. The I-V curves and potentiation and depression for LTM characteristics were studied. The switching and conduction mechanisms of Ni/Ag/AlN/TiN devices were analyzed using schematic drawings and energy band diagrams. The linearity of potentiation and depression was compared, and the accuracy of Modified National Institute of Standards and Technology (MNIST) pattern was evaluated based on the linearity.
JOURNAL OF ALLOYS AND COMPOUNDS
(2022)
Article
Materials Science, Ceramics
Yongyue Xiao, Xinjiang Wu, Yaoyao Jin, Guangsen Cao, Bei Jiang, Shanwu Ke, Cong Ye
Summary: A high-performance synaptic plasticity memristor based on Pt/HfO2/BiFeO3/HfO2/TiN structure was developed with excellent switching windows, uniformity, and durability achieved through N2 annealing treatment. The device demonstrated basic synaptic biomimetic function and was able to simulate handwritten digital image recognition using the MLP algorithm.
CERAMICS INTERNATIONAL
(2022)
Article
Chemistry, Physical
Minsu Park, Myounggon Kang, Sungjun Kim
Summary: The study compared the filamentary resistive switching and homogeneous resistive switching of the Ta/SiN/Si memristor device for the implementation of hardware-based neuromorphic system. It was found that the two switching modes have different performance and mechanisms, with the homogeneous resistive switching mode allowing for a more gradual conductance control and multi-level states property, providing better pattern recognition performance when applied to a neural network model. Additionally, improved frequency dependent conductance modulation was demonstrated in the homogeneous resistive switching mode.
JOURNAL OF ALLOYS AND COMPOUNDS
(2021)
Article
Chemistry, Physical
Wenxiao Wang, Nam-Young Kim, Dongmin Lee, Feifei Yin, Hongsen Niu, Enkhzaya Ganbold, Jae-Woo Park, Young-Kee Shin, Yang Li, Eun-Seong Kim
Summary: This research proposes a transparent optoelectronic memristor that replicates associative learning in the brain through neuromorphic computations. Basic synaptic plasticity is achieved with electric stimuli, and an artificial neural network is constructed, achieving high handwriting recognition accuracy. Light-induced synaptic plasticity and optoelectronic synergistic response are achieved through the photoelectrical effect and carrier dynamics. This work provides a platform for complex neuromorphic computing, with potential applications in adaptive intelligent sensing systems and autonomous robotics.
Article
Engineering, Electrical & Electronic
Jing Sun, Zhan Wang, Saisai Wang, Mei Yang, Haixia Gao, Hong Wang, Xiaohua Ma, Yue Hao
Summary: In this study, a transient form of diffusive memristors with a cross-point structure was proposed for physical unclonable functions (PUF). The introduction of Ag in the MgO switching layer increased randomness and achieved a high inter-chip Hamming distance. Furthermore, the transient PUF devices exhibited triggered failure after immersion in deionized water and could be transferred onto flexible substrates using a water-assisted method. This transient form of memristor provides valuable insights for next-generation PUF design and hardware security.
IEEE ELECTRON DEVICE LETTERS
(2022)
Article
Nanoscience & Nanotechnology
Momo Zhao, Saisai Wang, Dingwei Li, Rui Wang, Fanfan Li, Mengqi Wu, Kun Liang, Huihui Ren, Xiaorui Zheng, Chengchen Guo, Xiaohua Ma, Bowen Zhu, Hong Wang, Yue Hao
Summary: High-performance silk fibroin-based threshold switching (TS) memristors with outstanding device uniformity and stability, even under high humidity and temperature conditions, are demonstrated. These memristors exhibit short-term plasticity similar to biological synapses and successfully realize a leaky integrate-and-fire (LIF) artificial neuron based on their volatile characteristics, paving the way for advanced bioelectronics and neuromorphic computing.
ADVANCED ELECTRONIC MATERIALS
(2022)
Article
Chemistry, Multidisciplinary
Kun Liang, Rui Wang, Bingbing Huo, Huihui Ren, Dingwei Li, Yan Wang, Yingjie Tang, Yitong Chen, Chunyan Song, Fanfan Li, Botao Ji, Hong Wang, Bowen Zhu
Summary: This research reports a high-performance optoelectronic synaptic transistor based on hybrid heterostructures, which utilizes heavy-metal-free quantum dots and oxide semiconductor materials, achieving a high-accuracy artificial vision system and demonstrating important synaptic behaviors.
Article
Engineering, Electrical & Electronic
Kun Liang, Huihui Ren, Yan Wang, Dingwei Li, Yingjie Tang, Chunyan Song, Yitong Chen, Fanfan Li, Hong Wang, Bowen Zhu
Summary: This article achieves optoelectronic synaptic transistors with tunable plasticity by utilizing printed indium tin oxide (ITO) channel and Ag/ITO dual-layer contact electrodes. Both the short- and long-term plasticity in the printed ITO synaptic TFTs are emulated under the synergistic electrical and optical modulation. These achievements are of great significance for applying printed synaptic transistors in optoelectronic neuromorphic hardware.
IEEE ELECTRON DEVICE LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Yaxiong Cao, Saisai Wang, Jiaxing Lv, Fanfan Li, Qi Liang, Mei Yang, Xiaohua Ma, Hong Wang, Yue Hao
Summary: This article proposes a fully physically transient volatile memristor with Mg as an active electrode, demonstrating threshold switching performance and biological synapse plasticity. The device is fabricated on biodegradable and biocompatible substrates using a water-assisted transfer printing method. The study highlights the significance of the fully transient volatile memristor in security neuromorphic computing and bio-integrated electronic systems.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2022)
Article
Chemistry, Multidisciplinary
Rui Wang, Kun Liang, Saisai Wang, Yaxiong Cao, Yuhan Xin, Yaqian Peng, Xiaohua Ma, Bowen Zhu, Hong Wang, Yue Hao
Summary: Printed electronics has the potential to fabricate novel devices over a large area with low cost on nontraditional substrates, driving future data-intensive technologies. Physical unclonable functions (PUFs) offer a promising built-in hardware-security system comparable to biometrical data by utilizing intrinsic variations in the additive manufacturing process of active devices. However, printed PUFs still face challenges in achieving significant changes in devices to increase robustness to external environment noise.
ADVANCED MATERIALS
(2023)
Review
Nanoscience & Nanotechnology
Saisai Wang, Rui Wang, Yaxiong Cao, Xiaohua Ma, Hong Wang, Yue Hao
Summary: With the rapid development of AI technology, brain-computer interfaces are becoming a reality, which has great potential in the field of intelligent robots. Finding devices that can connect with living tissues is expected to achieve brain-computer interfaces and biological integration. Memristors based on brain-like neural networks can bridge electronic and biological nervous systems, and the development of bio-voltage memristors is reviewed, including artificial synaptic and neuronal functions as well as broad application prospects in neuromorphic computing and bio-electronic interfaces.
ADVANCED ELECTRONIC MATERIALS
(2023)
Article
Materials Science, Multidisciplinary
Yaxiong Cao, Saisai Wang, Rui Wang, Yuhan Xin, Yaqian Peng, Jing Sun, Mei Yang, Xiaohua Ma, Ling Lv, Hong Wang, Yue Hao
Summary: In this study, a fully biocompatible and biodegradable threshold switching (TS) memristor was proposed as an artificial nociceptor. The device exhibited stable electrical performance even under bending conditions. Important nociceptor behaviors were successfully demonstrated, and an optoelectronic nociceptor system was built. The devices, made on a biodegradable substrate, could completely dissolve in deionized water, mimicking the decomposition of necrotic tissue. This research provides a novel approach for developing fully biocompatible and biodegradable artificial nociceptors for applications in implantable and wearable electronics and bio-integrated systems.
SCIENCE CHINA-MATERIALS
(2023)
Article
Chemistry, Multidisciplinary
Yitong Chen, Min Zhang, Dingwei Li, Yingjie Tang, Huihui Ren, Jiye Li, Kun Liang, Yan Wang, Liaoyong Wen, Wenbin Li, Wei Kong, Shi Liu, Hong Wang, Donglin Wang, Bowen Zhu
Summary: We develop a bidirectional synaptic phototransistor based on a two-dimensional ferroelectric semiconductor, a-In2Se3, which exhibits bidirectional potentiated and depressed synaptic weight update under optical pulse stimulation and enables high accuracy color recognition of 97%.
Article
Physics, Applied
Jing Sun, Zhan Wang, Saisai Wang, Yaxiong Cao, Haixia Gao, Hong Wang, Xiaohua Ma, Yue Hao
Summary: In this study, a physically transient memristive device with configurable resistive switching functionality was demonstrated for security neuromorphic computing. The device exhibited typical synaptic functions and could instantly disappear in de-ionized water, providing potential perspectives on enhancing information security in neuromorphic computing systems.
APPLIED PHYSICS LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Yaxiong Cao, Saisai Wang, Rui Wang, Jing Sun, Mei Yang, Xiaohua Ma, Hong Wang, Yue Hao
Summary: This article proposes a physically transient artificial neuron based on TS devices, which has fast switching speed and high switching endurance, and successfully emulates the key functions of a biological neuron. In addition, the device can disintegrate completely after being soaked in DI water at RT for 40 min, paving the way for applications in secure neuromorphic computing, biointegrated electronics, and human-machine interfaces.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2023)
Review
Chemistry, Multidisciplinary
Rui Wang, Wanlin Zhang, Saisai Wang, Tonglong Zeng, Xiaohua Ma, Hong Wang, Yue Hao
Summary: This article systematically presents the requirements, mechanisms, and implementation methods of memristor-based compressed sensing (CS) technology, as well as its potential in all-in-one compression and encryption. The article points out the lack of a comprehensive overview of memristor-based CS techniques.
Article
Nanoscience & Nanotechnology
Rui Wang, Saisai Wang, Yuhan Xin, Yaxiong Cao, Yu Liang, Yaqian Peng, Jie Feng, Yang Li, Ling Lv, Xiaohua Ma, Hong Wang, Yue Hao
Summary: This article reports the application of polyimide (PI) threshold-switching memristors in a compression and encryption engine. The engine uses Gaussian conductance distribution for compressed sensing and randomly set voltages for improving security. It offers an excellent solution for ensuring the efficiency and security of IoT, with compression performance advantages and the ability to function in harsh environments.
Proceedings Paper
Engineering, Electrical & Electronic
Yingjie Tang, Dingwei Li, Yan Wang, Fanfan Li, Yitong Chen, Kun Liang, Huihui Ren, Chunyan Song, Hong Wang, Bowen Zhu
Summary: In this work, a flexible, transparent active-matrix tactile sensor array (TSA) is demonstrated by integrating solution-processed indium oxide thin-film transistor (TFT) array with a highly pressure-sensitive micro-pyramidal film. The integrated TFT-resistive sensor exhibits high sensitivity, fast response/recovery time, and robust mechanical flexibility. The optimized process enables the construction of a prototypical 10x10 AM-TSA for user-interactive sensor interfaces.
2022 INTERNATIONAL ELECTRON DEVICES MEETING, IEDM
(2022)
Article
Automation & Control Systems
Xulei Wu, Bingjie Dang, Hong Wang, Xiulong Wu, Yuchao Yang
Summary: In this study, memristor arrays with multilevel resistance states were fabricated for speech recognition in memristive spiking neural networks, demonstrating software-comparable accuracy. This could pave the way for building a bioinspired spike-based neuromorphic system.
ADVANCED INTELLIGENT SYSTEMS
(2022)
