Article
Chemistry, Multidisciplinary
Hyeongjun Kim, Seyong Oh, Hyongsuk Choo, Dong-Ho Kang, Jin-Hong Park
Summary: In this study, we propose a tactile neuromorphic system that utilizes a PDMS-based triboelectric sensor and a MoS2/P(VDF-TrFE) heterostructure-based ferroelectric synapse. This system can convert tactile stimuli into electrical signals in real time and exhibits exceptional long-term potentiation/depression characteristics. The maximum recognition rate achieved in training and recognition simulations using Morse code and MNIST handwritten digits was 96.17%.
Review
Chemistry, Multidisciplinary
Ik-Jyae Kim, Jang-Sik Lee
Summary: This review summarizes the recent developments in ferroelectric devices, particularly ferroelectric transistors, for next-generation memory and neuromorphic applications. It first reviews the types and operation mechanisms of ferroelectric memories, then discusses the issues limiting the realization of high-performance ferroelectric transistors and possible solutions. It also reviews the experimental demonstration of ferroelectric transistor arrays, including 3D ferroelectric NAND and its operation characteristics, and outlines the challenges and strategies towards the development of next-generation memory and neuromorphic applications based on ferroelectric transistors.
ADVANCED MATERIALS
(2023)
Article
Chemistry, Multidisciplinary
Yitong Chen, Dingwei Li, Huihui Ren, Yingjie Tang, Kun Liang, Yan Wang, Fanfan Li, Chunyan Song, Jiaqi Guan, Zhong Chen, Xingyu Lu, Guangwei Xu, Wenbin Li, Shi Liu, Bowen Zhu
Summary: This work presents the development of a polarized switch synaptic memtransistor based on a two-dimensional ferroelectric semiconductor α-In2Se3, which exhibits high accuracy and synaptic characteristics, providing new opportunities for neuromorphic computing.
Article
Chemistry, Multidisciplinary
Qiang Wang, Ren Luo, Yankun Wang, Wencheng Fang, Luyue Jiang, Yangyang Liu, Ruobing Wang, Liyan Dai, Jinyan Zhao, Jinshun Bi, Zenghui Liu, Libo Zhao, Zhuangde Jiang, Zhitang Song, Jutta Schwarzkopf, Thomas Schroeder, Shengli Wu, Zuo-Guang Ye, Wei Ren, Sannian Song, Gang Niu
Summary: The long-term plasticity of bio-synapses is important for information encoding and its emulation in neuromorphic computing. Ga-doped GST PCRAM devices demonstrate excellent long-term synaptic plasticity and high accuracy in image recognition tasks, highlighting their potential for future high-performance neuromorphic computing.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Chemistry, Multidisciplinary
Yurong Jiang, Linlin Zhang, Rui Wang, Hongzhi Li, Lin Li, Suicai Zhang, Xueping Li, Jian Su, Xiaohui Song, Congxin Xia
Summary: In this study, a reconfigurable two-dimensional MoS2 transistor with an asymmetric ferroelectric gate is designed, demonstrating high memory and logic capability. The device exhibits excellent nonvolatile characteristics, robust electric and optic cycling, and synaptic behavior in response to different light pulses, showcasing its potential for Fe-FETs in logic processing and nonvolatile memory applications.
Article
Nanoscience & Nanotechnology
Ruizhi Wang, Pengyue Chen, Dandan Hao, Junyao Zhang, Qianqian Shi, Dapeng Liu, Li Li, Lize Xiong, Junhe Zhou, Jia Huang
Summary: In this study, lead-free perovskite CsBi3I10 is used as a photoactive material to fabricate organic synaptic transistors with excellent stability and simulated synaptic functions. The devices exhibit promising performance for neuromorphic computing applications.
ACS APPLIED MATERIALS & INTERFACES
(2021)
Review
Chemistry, Multidisciplinary
Cheng Zhang, Mohan Chen, Yelong Pan, Yang Li, Kuaibing Wang, Junwei Yuan, Yanqiu Sun, Qichun Zhang
Summary: This review summarizes the recent advances and applications of carbon dot-based memristors in artificial synapses, neuromorphic computing, and human sensory perception systems. It introduces the synthetic methods and discusses the structure-property relationship and resistive switching mechanism of carbon dot-based memristors. The challenges and prospects of memristor-based artificial synapses and neuromorphic computing are presented, and promising application scenarios of carbon dot-based memristors are outlined.
Article
Chemistry, Physical
Enlong Li, Xiaomin Wu, Qizhen Chen, Shengyuan Wu, Lihua He, Rengjian Yu, Yuanyuan Hu, Huipeng Chen, Tailiang Guo
Summary: The NOFST, with its nanoscale channel length and unique operation mechanism, shows excellent gate control ability to improve fault tolerance and weight update properties. Use of this special device structure led to a record high recognition accuracy of 91.38% for organic field-effect synaptic transistors, offering a new pathway for developing organic neuromorphic hardware systems with high recognition accuracy.
Article
Nanoscience & Nanotechnology
Ching-Kang Shen, Rajneesh Chaurasiya, Kuan-Ting Chen, Jen-Sue Chen
Summary: In this study, a simple and cost-effective ferroelectric-coupled zinc-tin oxide thin-film transistor was reported for artificial synaptic devices. The device demonstrated superior artificial synapse responses and successfully emulated important features of synaptic behavior, highlighting its potential as a hardware candidate for neuromorphic computing.
ACS APPLIED MATERIALS & INTERFACES
(2022)
Article
Chemistry, Multidisciplinary
Nikitas Siannas, Christina Zacharaki, Polychronis Tsipas, Dong Jik Kim, Wassim Hamouda, Cosmin Istrate, Lucian Pintilie, Martin Schmidbauer, Catherine Dubourdieu, Athanasios Dimoulas
Summary: Synapses play a vital role in information processing, learning, and memory formation in the brain. Electronic synaptic devices that emulate the behavior of biological synapses have the potential to enable high-performance, energy-efficient, and scalable neuromorphic computing. A new ferroelectric device on silicon, a field-effect memristor, has been reported, which can access multiple states through the field-effect modulation in the semiconductor and dynamically adjust synaptic strength to mimic memory plasticity.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Nanoscience & Nanotechnology
Tao Guo, Jiawei Ge, Bai Sun, Kangqiang Pan, Zhao Pan, Lan Wei, Yong Yan, Y. Norman Zhou, Yimin A. Wu
Summary: Artificial synapses are crucial for neuromorphic computing chips, but existing biomaterial-based synapses face limitations. In this study, researchers developed a flexible and biocompatible artificial synapse using egg albumen@CuO material. This synapse successfully mimics biological synapse functions and achieved high accuracy in pattern recognition through neuromorphic computing simulations. The results provide insights for the development of biocompatible and wearable neuromorphic computing chips.
ADVANCED ELECTRONIC MATERIALS
(2022)
Review
Chemistry, Multidisciplinary
Geonyeop Lee, Ji-Hwan Baek, Fan Ren, Stephen J. Pearton, Gwan-Hyoung Lee, Jihyun Kim
Summary: Neuromorphic systems, which mimic neural functionalities of a human brain using artificial synapses and neurons, have advantages of high energy efficiency and fast computing speed. 2D materials, with unique surface properties and excellent crystallinity, have emerged as promising candidates for neuromorphic computing hardware due to uncontrollable defects in bulk material-based devices.
Review
Chemistry, Multidisciplinary
Gyeong-Tak Go, Yeongjun Lee, Dae-Gyo Seo, Tae-Woo Lee
Summary: Requirements and recent advances in organic neuroelectronics research are outlined. Organic materials are promising candidates for neural interfaces due to their mechanical softness, electrochemical properties, and biocompatibility. Organic nervetronics, which imitates the functional properties of the biological nerve system, is being developed to overcome the limitations of conventional neuroprosthetics.
ADVANCED MATERIALS
(2022)
Article
Chemistry, Multidisciplinary
YiLong Wang, Minghui Cao, Jing Bian, Qiang Li, Jie Su
Summary: In this study, a flexible synaptic memristor with ZnO nanosheets as the intermediate layer is successfully prepared. The device shows excellent switching characteristics and stable retention characteristic. By modulating the conductance, the memristor can simulate various synaptic plasticities. The neuromorphic system built from this memristor achieves high recognition accuracy for handwriting digit and maintains good performance under noise and bending.
ADVANCED FUNCTIONAL MATERIALS
(2022)
Article
Multidisciplinary Sciences
Hyunseok Shim, Seonmin Jang, Anish Thukral, Seongsik Jeong, Hyeseon Jo, Bin Kan, Shubham Patel, Guodan Wei, Wei Lan, Hae-Jin Kim, Cunjiang Yu
Summary: This paper introduces artificial neuromorphic cognitive skins based on elastomeric materials, which can achieve functions such as image memorization and programming through excitatory postsynaptic currents and paired-pulse facilitation index. It has important applications in robotics, wearables, skin prosthetics, and bioelectronics.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Multidisciplinary Sciences
Jingon Jang, Seonghoon Jang, Sanghyeon Choi, Gunuk Wang
Summary: By implementing additional filter evaluation and a simple run-off election-based decision rule in artificial neural network systems, confusion is reduced and training and inference performance is improved, leading to increased classification accuracy of the data set.
SCIENTIFIC REPORTS
(2021)
Article
Chemistry, Multidisciplinary
Haein Cho, Chan-Woo Jeon, Ba Da On, Il-Kyu Park, Sanghyeon Choi, Jingon Jang, Gunuk Wang
Summary: This study synthesized three Al-based LDHs with different divalent cations and investigated their electrical characteristics. The ZnAl-LDH junction showed distinct unipolar switching behavior with high transparency, making it a promising candidate for resistive switching devices.
ADVANCED MATERIALS INTERFACES
(2021)
Article
Chemistry, Multidisciplinary
Kyuho Lee, Hyowon Han, Youngwoo Kim, Jumi Park, Seonghoon Jang, Hyeokjung Lee, Seung Won Lee, HoYeon Kim, Yeeun Kim, Taebin Kim, Dongho Kim, Gunuk Wang, Cheolmin Park
Summary: This article presents an artificially intelligent photonic synapse with area-density-tunable perovskite nano-cone arrays for light reception, information storage, and learning. It shows potential for applications in emerging photo-interactive neuro-computing technologies, exhibiting accurate pattern recognition performances up to approximately 90%.
ADVANCED FUNCTIONAL MATERIALS
(2021)
Article
Chemistry, Physical
Sanghyeon Choi, Jae-Wan Choi, Jong Chan Kim, Hu Young Jeong, Jaeho Shin, Seonghoon Jang, Seonggil Ham, Nam Dong Kim, Gunuk Wang
Summary: This study presents a novel three-terminal memristor architecture by vertically integrating a gate-tunable SiOx memristor and graphene barristor, improving electrical performance and energy consumption. Additionally, nonvolatile universal logic gates were successfully implemented, showcasing the potential application of gate-tunable SiOx memristors in fast, low-energy electronic devices.
Article
Chemistry, Multidisciplinary
Jung Sun Eo, Jaeho Shin, Seunghoon Yang, Takgyeong Jeon, Jaeho Lee, Sanghyeon Choi, Chul-Ho Lee, Gunuk Wang
Summary: Understanding and designing the interfacial band alignment in a molecular heterojunction is crucial for achieving its desired electronic functionality. By controlling the interfacial band offset between a molecular self-assembled monolayer and a 2D semiconductor, a tailored molecular heterojunction selector can be implemented, affecting the nonlinearity of the device significantly.
Article
Chemistry, Multidisciplinary
Youngwoo Kim, Kyuho Lee, Junseok Lee, Seonghoon Jang, HoYeon Kim, Hyunhaeng Lee, Seung Won Lee, Gunuk Wang, Cheolmin Park
Summary: An artificially intelligent magnetoreceptive synapse inspired by birds’ magnetocognitive ability has been proposed in this study, which can be used as a synaptic compass for barrier-adaptable navigation and mapping through control of magnetic-field-dependent contact area and analog conductance modulation.
Article
Chemistry, Multidisciplinary
Sanghyeon Choi, Gwang Su Kim, Jehyeon Yang, Haein Cho, Chong-Yun Kang, Gunuk Wang
Summary: Researchers designed and fabricated a SiOx nanorod memristive device using the GLAD technique, proposing a controllable stochastic artificial neuron that mimics the signaling and dynamics of a biological neuron. By implementing ProbAct functions and electrical programming schemes, control over the neuron is achieved, allowing for probabilistic Bayesian inferences in genetic regulatory networks.
ADVANCED MATERIALS
(2022)
Article
Automation & Control Systems
Injune Yeo, Sang-Gyun Gi, Gunuk Wang, Byung-Geun Lee
Summary: This hardware- and power-efficient RRAM-based neural network is capable of online learning, achieving high classification accuracy and energy efficiency through modular design and neuron chip optimization. The network consists of 11 modules, each including RRAM arrays, analog multiplexers, and neuron chips, achieving a classification accuracy of 93.4% and energy efficiency of 38.1 TOPS/W.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Chemistry, Multidisciplinary
Sanghyeon Choi, Jingon Jang, Min Seob Kim, Nam Dong Kim, Jeehyun Kwag, Gunuk Wang
Summary: This study focuses on the implementation of a flexible neural network with probabilistic synapses as a first step towards an ultimate energy-efficient computing framework. A 16x16 crossbar array is designed and fabricated, utilizing a threshold-tunable and probabilistic SiOx memristive synaptic barristor with Si/graphene heterojunction. The suggested approach achieves significant reduction in learning energy while maintaining a high recognition accuracy.
Article
Multidisciplinary Sciences
Sunbin Hwang, Minji Kang, Aram Lee, Sukang Bae, Seoung-Ki Lee, Sang Hyun Lee, Takhee Lee, Gunuk Wang, Tae-Wook Kim
Summary: In this study, an integrated electronic fibre platform was developed by fabricating and integrating multiple electronic components onto a one-dimensional microfibre substrate. The platform showed high performance potential for wearable electronic textile systems, as demonstrated by its switching, data processing, and sensing capabilities.
NATURE COMMUNICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Jingon Jang, Sanggyun Gi, Injune Yeo, Sanghyeon Choi, Seonghoon Jang, Seonggil Ham, Byunggeun Lee, Gunuk Wang
Summary: Realization of memristor-based neuromorphic hardware system is important for energy efficient big data processing and artificial intelligence. Uniform and reliable titanium oxide (TiOx) memristor array devices are fabricated to enable vector-matrix multiplication process in a hardware neural network. A convolutional neural network hardware system using TiOx memristor arrays is designed and implemented, achieving learning rate modulation and fast convergence. This in situ training reduces training iterations and energy consumption while maintaining high classification accuracy.
Review
Chemistry, Multidisciplinary
Jaeho Shin, Jung Sun Eo, Takgyeong Jeon, Takhee Lee, Gunuk Wang
Summary: Molecular electronics, which involves using a single molecule or molecular ensemble to create functional electronic circuits, is an attractive research field. Recent efforts in this field have focused on diversifying electrical characteristics and device architectures by using various types of heterogeneous structures in molecular junctions. This review summarizes recent research on functional devices with molecular heterostructures, discusses their applicability and advantages, and highlights the challenges associated with implementing them in device applications.
Article
Chemistry, Physical
Jaeho Shin, Seunghoon Yang, Jung Sun Eo, Takgyeong Jeon, Jaeho Lee, Chul-Ho Lee, Gunuk Wang
Summary: This study reports a solid-state molecular photodiode device constructed using van der Waals heterojunctions, which can control light-responsive charge transport at the molecular scale and achieve bidirectional modulation of interface band alignment. Compared to other similar devices at a similar scale, these heterojunction devices exhibit significantly enhanced photo-responsive performances.
Article
Chemistry, Multidisciplinary
Young Ran Park, Gunuk Wang
Summary: This study designs and fabricates an artificial synapse based on a mixed-dimensional heterostructure for reconfigurable neuromorphic functions. The synapse exhibits high yield and reliable self-rectifying analog switching characteristics, improving the recognition accuracy of MNIST patterns while reducing energy consumption.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Engineering, Electrical & Electronic
Young Ran Park, Haein Cho, Gunuk Wang
Summary: In this study, a solution-processable two-terminal Ag (or Al)/NiOx/ITO memristor was demonstrated to exhibit triple-switching characteristics depending on the different voltage regimes. The three switching characteristics are related to the transition of dominant switching mechanisms depending on the operating scheme, which is investigated using various material and chemical characterization techniques.
ACS APPLIED ELECTRONIC MATERIALS
(2022)
