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Lee SW, Yun SY, Han JK, Nho YH, Jeon SB, Choi YK. Spike-Based Neuromorphic Hardware for Dynamic Tactile Perception with a Self-Powered Mechanoreceptor Array. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2402175. [PMID: 38981031 DOI: 10.1002/advs.202402175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 06/27/2024] [Indexed: 07/11/2024]
Abstract
A self-powered mechanoreceptor array is demonstrated using four mechanoreceptor cells for recognition of dynamic touch gestures. Each cell consists of a triboelectric nanogenerator (TENG) for touch sensing and a bi-stable resistor (biristor) for spike encoding. It produces informative spike signals by sensing a force of an external touch and encoding the force into the number of spikes. An array of the mechanoreceptor cells is utilized to monitor various touch gestures and it successfully generated spike signals corresponding to all the gestures. To validate the practicality of the mechanoreceptor array, a spiking neural network (SNN), highly attractive for power consumption compared to the conventional von Neumann architecture, is used for the identification of touch gestures. The measured spiking signals are reflected as inputs for the SNN simulations. Consequently, touch gestures are classified with a high accuracy rate of 92.5%. The proposed mechanoreceptor array emerges as a promising candidate for a building block of tactile in-sensor computing in the era of the Internet of Things (IoT), due to the low cost and high manufacturability of the TENG. This eliminates the need for a power supply, coupled with the intrinsic high throughput of the Si-based biristor employing complementary metal-oxide-semiconductor (CMOS) technology.
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Affiliation(s)
- Sang-Won Lee
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Seong-Yun Yun
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Joon-Kyu Han
- System Semiconductor Engineering and Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul, 04107, Republic of Korea
| | - Young-Hoon Nho
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Seung-Bae Jeon
- Department of Electronic Engineering, Hanbat National University, 125 Dongseo-daero, Yuseong-gu, Daejeon, 34158, Republic of Korea
| | - Yang-Kyu Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
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Ding QA, Gu C, Li J, Li X, Hou B, Peng Y, Chen B, Yao Y. Mimicking the retinal neuron functions by a photoresponsive single transistor with a double gate. Biophys J 2024; 123:1804-1814. [PMID: 38783604 PMCID: PMC11267426 DOI: 10.1016/j.bpj.2024.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/07/2024] [Accepted: 05/21/2024] [Indexed: 05/25/2024] Open
Abstract
To realize a low-cost neuromorphic visual system, employing an artificial neuron capable of mimicking the retinal neuron functions is essential. A photoresponsive single transistor neuron composed of a vertical silicon nanowire is proposed. Similar to retinal neurons, various photoresponsive characteristics of the single transistor neuron can be modulated by light intensity as well as wavelength and have a high responsivity to green light like the human eye. The device is designed with a cylindrical surrounding double-gate structure, enclosed by an independently controlled outer gate and inner gate. The outer gate has the function of selectively inhibiting neuron activity, which can mimic lateral inhibition of amacrine cells to ganglion cells, and the inner gate can be utilized for the adjustment of the firing threshold voltage, which can be used to mimic the regulation of photoresponsivity by horizontal cells for adaptive visual perception. Furthermore, a myelination function that controls the speed of information transmission is obtained according to the inherent asymmetric source/drain structure of a vertical silicon nanowire. This work can enable photoresponsive neuronal function using only a single transistor, providing a promising hardware implementation for building miniaturized neuromorphic vision systems at low cost.
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Affiliation(s)
- Qing-An Ding
- School of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Chaoran Gu
- School of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Jianyu Li
- School of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Xiaoyuan Li
- School of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China; Affiliated Hospital of Qingdao University, Qingdao, China.
| | - BingHui Hou
- Affiliated Hospital of Qingdao University, Qingdao, China.
| | - Yandong Peng
- School of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China.
| | - Bing Chen
- School of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Youli Yao
- School of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
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Yu JM, Kim Y, Lee C, Jeong B, Kim JK, Han JK, Yang J, Yun SY, Im SG, Choi YK. Bio-Inspired Organic Synaptor with In Situ Ion-Doped Ultrathin Polyelectrolyte Containing Acetylcholine-Like Cation. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2312283. [PMID: 38409517 DOI: 10.1002/smll.202312283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/14/2024] [Indexed: 02/28/2024]
Abstract
An ion-based synaptic transistor (synaptor) is designed to emulate a biological synapse using controlled ion movements. However, developing a solid-state electrolyte that can facilitate ion movement while achieving large-scale integration remains challenging. Here, a bio-inspired organic synaptor (BioSyn) with an in situ ion-doped polyelectrolyte (i-IDOPE) is demonstrated. At the molecular scale, a polyelectrolyte containing the tert-amine cation, inspired by the neurotransmitter acetylcholine is synthesized using initiated chemical vapor deposition (iCVD) with in situ doping, a one-step vapor-phase deposition used to fabricate solid-state electrolytes. This method results in an ultrathin, but highly uniform and conformal solid-state electrolyte layer compatible with large-scale integration, a form that is not previously attainable. At a synapse scale, synapse functionality is replicated, including short-term and long-term synaptic plasticity (STSP and LTSP), along with a transformation from STSP to LTSP regulated by pre-synaptic voltage spikes. On a system scale, a reflex in a peripheral nervous system is mimicked by mounting the BioSyns on various substrates such as rigid glass, flexible polyethylene naphthalate, and stretchable poly(styrene-ethylene-butylene-styrene) for a decentralized processing unit. Finally, a classification accuracy of 90.6% is achieved through semi-empirical simulations of MNIST pattern recognition, incorporating the measured LTSP characteristics from the BioSyns.
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Affiliation(s)
- Ji-Man Yu
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Youson Kim
- Department of Chemical and Biomolecular Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Changhyeon Lee
- Department of Chemical and Biomolecular Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Booseok Jeong
- Department of Chemical and Biomolecular Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jin-Ki Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Joon-Kyu Han
- System Semiconductor Engineering and Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul, 04107, Republic of Korea
| | - Junyeong Yang
- Department of Chemical and Biomolecular Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Seong-Yun Yun
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Sung Gap Im
- Department of Chemical and Biomolecular Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Yang-Kyu Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
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Zhang X, Liu D, Liu S, Cai Y, Shan L, Chen C, Chen H, Liu Y, Guo T, Chen H. Toward Intelligent Display with Neuromorphic Technology. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2401821. [PMID: 38567884 DOI: 10.1002/adma.202401821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/19/2024] [Indexed: 04/16/2024]
Abstract
In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full-scene display and realistic display. Incorporating "intelligence" into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light-emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all-in-one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross-scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full-scene display and realistic display with high efficiency and low power consumption. This review offers a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. This work delves into its state-of-the-art designs and potential future developments aimed at revolutionizing display technology.
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Affiliation(s)
- Xianghong Zhang
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Di Liu
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Shuai Liu
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Yongjie Cai
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Liuting Shan
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Cong Chen
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Huimei Chen
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Yaqian Liu
- School of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou, Henan, 450002, China
| | - Tailiang Guo
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Huipeng Chen
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
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5
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Duan S, Zhang X, Xi Y, Liu D, Zhang X, Li C, Jiang L, Li L, Chen H, Ren X, Hu W. Solution-Processed Ultralow Voltage Organic Transistors With Sharp Switching for Adaptive Visual Perception. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2405030. [PMID: 38808576 DOI: 10.1002/adma.202405030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/26/2024] [Indexed: 05/30/2024]
Abstract
Neuromorphic visual systems can emulate biological retinal systems to perceive visual information under different levels of illumination, making them have considerable potential for future intelligent vehicles and vision automation. However, the complex circuits and high operating voltages of conventional artificial vision systems present great challenges for device integration and power consumption. Here, bioinspired synaptic transistors based on organic single crystal phototransistors are reported, which exhibit excitation and inhibition synaptic plasticity with time-varying. By manipulating the charge dynamics of the trapping centers of organic crystal-electret vertical stacks, organic transistors can operate below 1 V with record high on/off ratios close to 108 and sharp switching with a subthreshold swing of 59.8 mV dec-1. Moreover, the approach offers visual adaptation with highly localized modulation and over 98.2% recognition accuracy under different illumination levels. These bioinspired visual adaptation transistors offer great potential for simplifying the circuitry of artificial vision systems and will contribute to the development of machine vision applications.
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Affiliation(s)
- Shuming Duan
- Key Laboratory of Organic Integrated Circuits, Ministry of Education, Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University and Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou, 350207, China
| | - Xianghong Zhang
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
| | - Yue Xi
- Key Laboratory of Organic Integrated Circuits, Ministry of Education, Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University and Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China
| | - Di Liu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
| | - Xiaotao Zhang
- Key Laboratory of Organic Integrated Circuits, Ministry of Education, Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University and Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China
| | - Chunlei Li
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China
| | - Lang Jiang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China
| | - Liqiang Li
- Key Laboratory of Organic Integrated Circuits, Ministry of Education, Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University and Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou, 350207, China
| | - Huipeng Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
| | - Xiaochen Ren
- Key Laboratory of Organic Integrated Circuits, Ministry of Education, Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University and Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China
| | - Wenping Hu
- Key Laboratory of Organic Integrated Circuits, Ministry of Education, Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University and Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, 300072, China
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou, 350207, China
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Lee DH, Lim T, Pyeon J, Park H, Lee SW, Lee S, Kim W, Kim M, Lee JC, Kim DW, Han S, Kim H, Park S, Choi YK. Self-Mixed Biphasic Liquid Metal Composite with Ultra-High Stretchability and Strain-Insensitivity for Neuromorphic Circuits. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2310956. [PMID: 38196140 DOI: 10.1002/adma.202310956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/29/2023] [Indexed: 01/11/2024]
Abstract
Neuromorphic circuits that can function under extreme deformations are important for various data-driven wearable and robotic applications. Herein, biphasic liquid metal particle (BMP) with unprecedented stretchability and strain-insensitivity (ΔR/R0 = 1.4@ 1200% strain) is developed to realize a stretchable neuromorphic circuit that mimics a spike-based biologic sensory system. The BMP consists of liquid metal particles (LMPs) and rigid liquid metal particles (RLMPs), which are homogeneously mixed via spontaneous solutal-Marangoni mixing flow during coating. This permits facile single step patterning directly on various substrates at room temperature. BMP is highly conductive (2.3 × 106 S/m) without any post activation steps. BMP interconnects are utilized for a sensory system, which is capable of distinguishing variations of biaxial strains with a spiking neural network, thus demonstrating their potential for various sensing and signal processing applications.
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Affiliation(s)
- Do Hoon Lee
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Taesu Lim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jeongsu Pyeon
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Hyunmin Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Sang-Won Lee
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Seungkyu Lee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Wonsik Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Min Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jeong-Chan Lee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Do-Wan Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Seungmin Han
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Hyoungsoo Kim
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Steve Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Yang-Kyu Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
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Jeon JW, Park B, Jang YH, Lee SH, Jeon S, Han J, Ryoo SK, Kim KD, Shim SK, Cheong S, Choi W, Jeon G, Kim S, Yoo C, Han JK, Hwang CS. Vertically Stackable Ovonic Threshold Switch Oscillator Using Atomic Layer Deposited Ge 0.6Se 0.4 Film for High-Density Artificial Neural Networks. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38491936 DOI: 10.1021/acsami.3c18625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2024]
Abstract
Nanodevice oscillators (nano-oscillators) have received considerable attention to implement in neuromorphic computing as hardware because they can significantly improve the device integration density and energy efficiency compared to complementary metal oxide semiconductor circuit-based oscillators. This work demonstrates vertically stackable nano-oscillators using an ovonic threshold switch (OTS) for high-density neuromorphic hardware. A vertically stackable Ge0.6Se0.4 OTS-oscillator (VOTS-OSC) is fabricated with a vertical crossbar array structure by growing Ge0.6Se0.4 film conformally on a contact hole structure using atomic layer deposition. The VOTS-OSC can be vertically integrated onto peripheral circuits without causing thermal damage because the fabrication temperature is <400 °C. The fabricated device exhibits oscillation characteristics, which can serve as leaky integrate-and-fire neurons in spiking neural networks (SNNs) and coupled oscillators in oscillatory neural networks (ONNs). For practical applications, pattern recognition and vertex coloring are demonstrated with SNNs and ONNs, respectively, using semiempirical simulations. This structure increases the oscillator integration density significantly, enabling complex tasks with a large number of oscillators. Moreover, it can enhance the computational speed of neural networks due to its rapid switching speed.
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Affiliation(s)
- Jeong Woo Jeon
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Byongwoo Park
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Yoon Ho Jang
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Soo Hyung Lee
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Sangmin Jeon
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Janguk Han
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Seung Kyu Ryoo
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Kyung Do Kim
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Sung Keun Shim
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Sunwoo Cheong
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Wonho Choi
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Gwangsik Jeon
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Sungjin Kim
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Chanyoung Yoo
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
| | - Joon-Kyu Han
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehagdong, Gwanak-gu, Seoul 08826, Republic of Korea
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Sung SH, Suh JM, Hwang YJ, Jang HW, Park JG, Jun SC. Data-centric artificial olfactory system based on the eigengraph. Nat Commun 2024; 15:1211. [PMID: 38332010 PMCID: PMC10853498 DOI: 10.1038/s41467-024-45430-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 01/23/2024] [Indexed: 02/10/2024] Open
Abstract
Recent studies of electronic nose system tend to waste significant amount of important data in odor identification. Until now, the sensitivity-oriented data composition has made it difficult to discover meaningful data to apply artificial intelligence in terms of in-depth analysis for odor attributes specifying the identities of gas molecules, ultimately resulting in hindering the advancement of the artificial olfactory technology. Here, we realize a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms by formally defining and utilizing the concept of Eigengraph in electrochemisty. The implicit odor attributes of the eigengraphs were mathematically substantialized as the Fourier transform-based Mel-Frequency Cepstral Coefficient feature vectors. Their effectiveness and applicability in deep learning processes for gas classification have been clearly demonstrated through experiments on complex mixed gases and automobile exhaust gases. We suggest that our findings can be widely applied as source technologies to develop standardized artificial olfactory systems.
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Affiliation(s)
- Seung-Hyun Sung
- School of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea
- Finance Division, Daejeon Metropolitan Office of Education, Daejeon, 35239, Republic of Korea
| | - Jun Min Suh
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Yun Ji Hwang
- School of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea.
- Advanced Institute of Convergence Technology, Seoul National University, Suwon, 16229, Republic of Korea.
| | - Jeon Gue Park
- Artificial Intelligence Laboratory, Tutorus Labs Inc., Seoul, 06595, Republic of Korea.
- Center for Educational Research, College of Education, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Seong Chan Jun
- School of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
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9
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Qiu E, Zhang YH, Ventra MD, Schuller IK. Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306818. [PMID: 37770043 DOI: 10.1002/adma.202306818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/25/2023] [Indexed: 10/03/2023]
Abstract
While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called "thermal neuristors", which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. This study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.
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Affiliation(s)
- Erbin Qiu
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Yuan-Hang Zhang
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
| | | | - Ivan K Schuller
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
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10
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Sunny MM, Thamankar R. Spike rate dependent synaptic characteristics in lamellar, multilayered alpha-MoO 3 based two-terminal devices - efficient way to control the synaptic amplification. RSC Adv 2024; 14:2518-2528. [PMID: 38226148 PMCID: PMC10788777 DOI: 10.1039/d3ra07757h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 12/19/2023] [Indexed: 01/17/2024] Open
Abstract
Brain-inspired computing systems require a rich variety of neuromorphic devices using multi-functional materials operating at room temperature. Artificial synapses which can be operated using optical and electrical stimuli are in high demand. In this regard, layered materials have attracted a lot of attention due to their tunable energy gap and exotic properties. In the current study, we report the growth of layered MoO3 using the chemical vapor deposition (CVD) technique. MoO3 has an energy gap of 3.22 eV and grows with a large aspect ratio, as seen through optical and scanning electron microscopy. We used transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy for complete characterisation. The two-terminal devices using platinum (Pt/MoO3/Pt) exhibit superior memory with the high-resistance state (HRS) and low-resistance state (LRS) differing by a large resistance (∼MΩ). The devices also show excellent synaptic characteristics. Both optical and electrical pulses can be utilised to stimulate the synapse. Consistent learning (potentiation) and forgetting (depression) curves are measured. Transition from long term depression to long term potentiation can be achieved using the spike frequency dependent pulsing scheme. We have found that the amplification of postsynaptic current can be tuned using such frequency dependent spikes. This will help us to design neuromorphic devices with the required synaptic amplification.
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Affiliation(s)
- Meenu Maria Sunny
- Department of Physics, Vellore Institute of Technology Vellore TN India
- Centre for Functional Materials, Vellore Institute of Technology Vellore TN India
| | - R Thamankar
- Centre for Functional Materials, Vellore Institute of Technology Vellore TN India
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11
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Wang H, Lu Y, Liu S, Yu J, Hu M, Li S, Yang R, Watanabe K, Taniguchi T, Ma Y, Miao X, Zhuge F, He Y, Zhai T. Adaptive Neural Activation and Neuromorphic Processing via Drain-Injection Threshold-Switching Float Gate Transistor Memory. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2309099. [PMID: 37953691 DOI: 10.1002/adma.202309099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/06/2023] [Indexed: 11/14/2023]
Abstract
Hetero-modulated neural activation is vital for adaptive information processing and learning that occurs in brain. To implement brain-inspired adaptive processing, previously various neurotransistors oriented for synaptic functions are extensively explored, however, the emulation of nonlinear neural activation and hetero-modulated behaviors are not possible due to the lack of threshold switching behavior in a conventional transistor structure. Here, a 2D van der Waals float gate transistor (FGT) that exhibits steep threshold switching behavior, and the emulation of hetero-modulated neuron functions (integrate-and-fire, sigmoid type activation) for adaptive sensory processing, are reported. Unlike conventional FGTs, the threshold switching behavior stems from impact ionization in channel and the coupled charge injection to float gate. When a threshold is met, a sub-30 mV dec-1 increase of transistor conductance by more than four orders is triggered with a typical switch time of approximately milliseconds. Essentially, by feeding light sensing signal as the modulation input, it is demonstrated that two typical tasks that rely on adaptive neural activation, including collision avoidance and adaptive visual perception, can be realized. These results may shed light on the emulation of rich hetero-modulating behaviors in biological neurons and the realization of biomimetic neuromorphic processing at low hardware cost.
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Affiliation(s)
- Han Wang
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Yuanlong Lu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Shangbo Liu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Jun Yu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Man Hu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Sainan Li
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Rui Yang
- Hubei Yangtze Memory Laboratory, School of Integrated circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Kenji Watanabe
- Research Center for Electronic and Optical Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, 305-0044, Japan
| | - Takashi Taniguchi
- Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, Namiki, Tsukuba, 305-0044, Japan
| | - Ying Ma
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Xiangshui Miao
- Hubei Yangtze Memory Laboratory, School of Integrated circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Fuwei Zhuge
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Yuhui He
- Hubei Yangtze Memory Laboratory, School of Integrated circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Tianyou Zhai
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
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12
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Liu L, Dananjaya PA, Ang CCI, Koh EK, Lim GJ, Poh HY, Chee MY, Lee CXX, Lew WS. A bi-functional three-terminal memristor applicable as an artificial synapse and neuron. NANOSCALE 2023; 15:17076-17084. [PMID: 37847400 DOI: 10.1039/d3nr02780e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
Abstract
Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show promise in handling spatiotemporal information with high time and energy efficiency. Two-terminal memristors have the capability to achieve both synaptic and neuronal functions; however, such memristors face asynchronous programming/reading operation issues. Here, a three-terminal memristor (3TM) based on oxygen ion migration is developed to function as both a synapse and a neuron. We demonstrate short-term plasticity such as pair-pulse facilitation and high-pass dynamic filtering in our devices. Additionally, a 'learning-forgetting-relearning' behavior is successfully mimicked, with lower power required for the relearning process than the first learning. Furthermore, by leveraging the short-term dynamics, the leaky-integrate-and-fire neuronal model is emulated by the 3TM without adopting an external capacitor to obtain the leakage property. The proposed bi-functional 3TM offers more process compatibility for integrating synaptic and neuronal components in the hardware implementation of an SNN.
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Affiliation(s)
- Lingli Liu
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Putu Andhita Dananjaya
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Calvin Ching Ian Ang
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Eng Kang Koh
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Gerard Joseph Lim
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Han Yin Poh
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Mun Yin Chee
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Calvin Xiu Xian Lee
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Wen Siang Lew
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
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13
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Zhao J, Ran Y, Pei Y, Wei Y, Sun J, Zhang Z, Wang J, Zhou Z, Wang Z, Sun Y, Yan X. Memristors based on NdNiO 3 nanocrystals film as sensory neurons for neuromorphic computing. MATERIALS HORIZONS 2023; 10:4521-4531. [PMID: 37555245 DOI: 10.1039/d3mh00835e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
By mimicking the behavior of the human brain, artificial neural systems offer the possibility to further improve computing efficiency and solve the von Neumann bottleneck. In particular, neural systems with perceptual capability expand the application field and lay a good foundation for the construction of perceptual storage and computational systems. However, research on neurons with perceptual functions is still relatively scarce, with most works focusing on optoelectronic synapses. The neuron is important for neuromorphic computing systems because neurons output excitatory or inhibitory stimuli to regulate the weight of synapses. Therefore, the construction of sensory neurons is crucial to expand the application range of brain-like neural computing. Here, an artificial sensory neuron is proposed, which is constructed using a photosensitive bipolar threshold switching memristor based on NdNiO3 (NNO) nanocrystals. These metallic phase nanocrystals can not only enhance the local electric field, but also act as a reservoir for defects (VoS) to guide the growth of conductive filaments and stabilize the performance of the device. They present stable bipolar threshold switching behavior with a low 120 nW set power, and the operating voltages decreased in light due to photocarrier action. A leaky integrate firing (LIF) neuron has been realized, which achieved key biological neuron functions, such as all-or-nothing spiking, threshold-driven firing, refractory period, and spiking frequency modulation. The LIF neurons receiving optical inputs have the properties of an artificial sensory neuron. It could regulate the spiking output frequency at different light densities, which could be used for a ship approaching a port. This work provides a promising hardware implementation towards constructing high-performance artificial intelligence to assist ships at night in a sensory system.
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Affiliation(s)
- Jianhui Zhao
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Yunfeng Ran
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Yifei Pei
- Hebei Key Laboratory of Optic-Electronic Information Materials, College of Physics Science and Technology, Hebei University, Baoding 071002, People's Republic of China
| | - Yiheng Wei
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Jiameng Sun
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Zixuan Zhang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Jiacheng Wang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Zhenyu Zhou
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Zhongrong Wang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Yong Sun
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
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14
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Schegolev AE, Klenov NV, Gubochkin GI, Kupriyanov MY, Soloviev II. Bio-Inspired Design of Superconducting Spiking Neuron and Synapse. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2101. [PMID: 37513112 PMCID: PMC10383304 DOI: 10.3390/nano13142101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]
Abstract
The imitative modelling of processes in the brain of living beings is an ambitious task. However, advances in the complexity of existing hardware brain models are limited by their low speed and high energy consumption. A superconducting circuit with Josephson junctions closely mimics the neuronal membrane with channels involved in the operation of the sodium-potassium pump. The dynamic processes in such a system are characterised by a duration of picoseconds and an energy level of attojoules. In this work, two superconducting models of a biological neuron are studied. New modes of their operation are identified, including the so-called bursting mode, which plays an important role in biological neural networks. The possibility of switching between different modes in situ is shown, providing the possibility of dynamic control of the system. A synaptic connection that mimics the short-term potentiation of a biological synapse is developed and demonstrated. Finally, the simplest two-neuron chain comprising the proposed bio-inspired components is simulated, and the prospects of superconducting hardware biosimilars are briefly discussed.
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Affiliation(s)
- Andrey E Schegolev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Nikolay V Klenov
- Faculty of Physics, Moscow State University, 119991 Moscow, Russia
- Faculty of Physics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
| | - Georgy I Gubochkin
- Faculty of Physics, Moscow State University, 119991 Moscow, Russia
- Russian Quantum Center, 100 Novaya Street, Skolkovo, 143025 Moscow, Russia
| | - Mikhail Yu Kupriyanov
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Igor I Soloviev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
- Faculty of Physics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
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15
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Hadiyal K, Ganesan R, Rastogi A, Thamankar R. Bio-inspired artificial synapse for neuromorphic computing based on NiO nanoparticle thin film. Sci Rep 2023; 13:7481. [PMID: 37160948 PMCID: PMC10169867 DOI: 10.1038/s41598-023-33752-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 04/18/2023] [Indexed: 05/11/2023] Open
Abstract
The unprecedented need for data processing in the modern technological era has created opportunities in neuromorphic devices and computation. This is primarily due to the extensive parallel processing done in our human brain. Data processing and logical decision-making at the same physical location are an exciting aspect of neuromorphic computation. For this, establishing reliable resistive switching devices working at room temperature with ease of fabrication is important. Here, a reliable analog resistive switching device based on Au/NiO nanoparticles/Au is discussed. The application of positive and negative voltage pulses of constant amplitude results in enhancement and reduction of synaptic current, which is consistent with potentiation and depression, respectively. The change in the conductance resulting in such a process can be fitted well with double exponential growth and decay, respectively. Consistent potentiation and depression characteristics reveal that non-ideal voltage pulses can result in a linear dependence of potentiation and depression. Long-term potentiation (LTP) and Long-term depression (LTD) characteristics have been established, which are essential for mimicking the biological synaptic applications. The NiO nanoparticle-based devices can also be used for controlled synaptic enhancement by optimizing the electric pulses, displaying typical learning-forgetting-relearning characteristics.
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Affiliation(s)
- Keval Hadiyal
- Centre for Functional Materials, Vellore Institute of Technology, Vellore, TN, 632014, India
- Department of Physics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, TN, 632014, India
| | - Ramakrishnan Ganesan
- Department of Chemistry, Birla Institute of Technology and Science (BITS), Pilani, Hyderabad Campus, Jawahar Nagar, Kapra Mandal, Medchal District, Hyderabad, Telangana, 500078, India
| | - A Rastogi
- Centre for Functional Materials, Vellore Institute of Technology, Vellore, TN, 632014, India
| | - R Thamankar
- Centre for Functional Materials, Vellore Institute of Technology, Vellore, TN, 632014, India.
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16
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Shin W, Im J, Koo RH, Kim J, Kwon KR, Kwon D, Kim JJ, Lee JH, Kwon D. Self-Curable Synaptic Ferroelectric FET Arrays for Neuromorphic Convolutional Neural Network. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2207661. [PMID: 36973600 DOI: 10.1002/advs.202207661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/20/2023] [Indexed: 05/27/2023]
Abstract
With the recently increasing prevalence of deep learning, both academia and industry exhibit substantial interest in neuromorphic computing, which mimics the functional and structural features of the human brain. To realize neuromorphic computing, an energy-efficient and reliable artificial synapse must be developed. In this study, the synaptic ferroelectric field-effect-transistor (FeFET) array is fabricated as a component of a neuromorphic convolutional neural network. Beyond the single transistor level, the long-term potentiation and depression of synaptic weights are achieved at the array level, and a successful program-inhibiting operation is demonstrated in the synaptic array, achieving a learning accuracy of 79.84% on the Canadian Institute for Advanced Research (CIFAR)-10 dataset. Furthermore, an efficient self-curing method is proposed to improve the endurance of the FeFET array by tenfold, utilizing the punch-through current inherent to the device. Low-frequency noise spectroscopy is employed to quantitatively evaluate the curing efficiency of the proposed self-curing method. The results of this study provide a method to fabricate and operate reliable synaptic FeFET arrays, thereby paving the way for further development of ferroelectric-based neuromorphic computing.
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Affiliation(s)
- Wonjun Shin
- Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jiyong Im
- Department of Electronic Engineering, Hanyang University, Seoul, 04763, South Korea
| | - Ryun-Han Koo
- Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jaehyeon Kim
- Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Ki-Ryun Kwon
- Department of Electronic Engineering, Hanyang University, Seoul, 04763, South Korea
| | - Dongseok Kwon
- Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jae-Joon Kim
- Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jong-Ho Lee
- Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
- Present address: Ministry of Science and ICT, Sejong, 30121, Republic of Korea
| | - Daewoong Kwon
- Department of Electronic Engineering, Hanyang University, Seoul, 04763, South Korea
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17
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Li J, Li M, Chen Z, Shao S, Gu W, Gu Y, Fang Y, Zhao J. Large area roll-to-roll printed semiconducting carbon nanotube thin films for flexible carbon-based electronics. NANOSCALE 2023; 15:5317-5326. [PMID: 36811360 DOI: 10.1039/d2nr07209b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A universal roll-to-roll (R2R) printing approach was developed to construct large area (8 cm × 14 cm) semiconducting single-walled carbon nanotube (sc-SWCNT) thin films on flexible substrates (such as polyethylene terephthalate (PET), paper, and Al foils) at a printing speed of 8 m min-1 using highly concentrated sc-SWCNT inks and crosslinked poly-4-vinylphenol (c-PVP) as the adhesion layer. Bottom-gated and top-gated flexible printed p-type TFTs based on R2R printed sc-SWCNT thin films exhibited good electrical properties with a carrier mobility of ∼11.9 cm2 V-1 s-1, Ion/Ioff ratios of ∼106, small hysteresis, and a subthreshold swing (SS) of 70-80 mV dec-1 at low gate operating voltages (±1 V), and excellent mechanical flexibility. Furthermore, the flexible printed complementary metal oxide semiconductor (CMOS) inverters demonstrated rail-to-rail voltage output characteristics under an operating voltage as low as VDD = -0.2 V, a voltage gain of 10.8 at VDD = -0.8 V, and power consumption as low as 0.056 nW at VDD = -0.2 V. To the best of our knowledge, the electrical properties of the printed SWCNT TFTs (such as Ion/Ioff ratio, mobility, operating voltage, and mechanical flexibility) and printed CMOS inverters based on the R2R printed sc-SWCNT active layer in this work are excellent compared to those of R2R printed SWCNT TFTs reported in the literature. Consequently, the universal R2R printing method reported in this work could promote the development of fully printed low-cost, large-area, high-output, and flexible carbon-based electronics.
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Affiliation(s)
- Jiaqi Li
- 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
| | - Zhaofeng Chen
- 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
| | - 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
| | - Weibing Gu
- 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
| | - Ying Gu
- 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
| | - 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
| | - 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
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18
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Han JK, Yun SY, Yu JM, Jeon SB, Choi YK. Artificial Multisensory Neuron with a Single Transistor for Multimodal Perception through Hybrid Visual and Thermal Sensing. ACS APPLIED MATERIALS & INTERFACES 2023; 15:5449-5455. [PMID: 36669163 DOI: 10.1021/acsami.2c19208] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
An artificial multisensory device applicable to in-sensor computing is demonstrated with a single-transistor neuron (1T-neuron) for multimodal perception. It simultaneously receives two sensing signals from visual and thermal stimuli. The 1T-neuron transforms these signals into electrical signals in the form of spiking and then fires them for a spiking neural network at the same time. This feature makes it feasible to realize input neurons for multimodal sensing. Visual and thermal sensing is achieved due to the inherent optical and thermal behaviors of the 1T-neuron. To demonstrate a neuromorphic multimodal sensing system with the artificial multisensory 1T-neuron, fingerprint recognition, widely used for biometric security, is implemented. Owing to the simultaneous sensing of heat as well as light, the proposed fingerprint recognition system composed of multisensory 1T-neurons not only identifies a genuine pattern but also judges whether or not it is forged.
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Affiliation(s)
- Joon-Kyu Han
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon34141, Republic of Korea
| | - Seong-Yun Yun
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon34141, Republic of Korea
| | - Ji-Man Yu
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon34141, Republic of Korea
| | - Seung-Bae Jeon
- Department of Electronic Engineering, Hanbat National University, 125 Dongseo-daero, Yuseong-gu, Daejeon34158, Republic of Korea
| | - Yang-Kyu Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon34141, Republic of Korea
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19
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Li H, Hu J, Chen A, Wang C, Chen L, Tian F, Zhou J, Zhao Y, Chen J, Tong Y, Loh KP, Xu Y, Zhang Y, Hasan T, Yu B. Single-Transistor Neuron with Excitatory-Inhibitory Spatiotemporal Dynamics Applied for Neuronal Oscillations. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2207371. [PMID: 36217845 DOI: 10.1002/adma.202207371] [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: 08/12/2022] [Revised: 10/02/2022] [Indexed: 06/16/2023]
Abstract
Brain-inspired neuromorphic computing systems with the potential to drive the next wave of artificial intelligence demand a spectrum of critical components beyond simple characteristics. An emerging research trend is to achieve advanced functions with ultracompact neuromorphic devices. In this work, a single-transistor neuron is demonstrated that implements excitatory-inhibitory (E-I) spatiotemporal integration and a series of essential neuron behaviors. Neuronal oscillations, the fundamental mode of neuronal communication, that construct high-dimensional population code to achieve efficient computing in the brain, can also be demonstrated by the neuron transistors. The highly scalable E-I neuron can be the basic building block for implementing core neuronal circuit motifs and large-scale architectural plans to replicate energy-efficient neural computations, forming the foundation of future integrated neuromorphic systems.
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Affiliation(s)
- Hanxi Li
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Jiayang Hu
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Anzhe Chen
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Chenhao Wang
- School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Li Chen
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Feng Tian
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
- Joint Institute of Zhejiang University and University of Illinois at Urbana-Champaign, Zhejiang University, Haining, 314400, China
| | - Jiachao Zhou
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Yuda Zhao
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Jinrui Chen
- Cambridge Graphene Centre, Cambridge University Engineering Department, Cambridge, CB3 0FA, UK
| | - Yi Tong
- Technology Development Department, Gusu Laboratory of Materials, Suzhou, 215000, China
| | - Kian Ping Loh
- Department of Chemistry, National University of Singapore, Singapore, 119077, Singapore
| | - Yang Xu
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
- Joint Institute of Zhejiang University and University of Illinois at Urbana-Champaign, Zhejiang University, Haining, 314400, China
| | - Yishu Zhang
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Tawfique Hasan
- Cambridge Graphene Centre, Cambridge University Engineering Department, Cambridge, CB3 0FA, UK
| | - Bin Yu
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
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20
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Sebastian A, Pendurthi R, Kozhakhmetov A, Trainor N, Robinson JA, Redwing JM, Das S. Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks. Nat Commun 2022; 13:6139. [PMID: 36253370 PMCID: PMC9576759 DOI: 10.1038/s41467-022-33699-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 09/27/2022] [Indexed: 12/24/2022] Open
Abstract
Artificial neural networks have demonstrated superiority over traditional computing architectures in tasks such as pattern classification and learning. However, they do not measure uncertainty in predictions, and hence they can make wrong predictions with high confidence, which can be detrimental for many mission-critical applications. In contrast, Bayesian neural networks (BNNs) naturally include such uncertainty in their model, as the weights are represented by probability distributions (e.g. Gaussian distribution). Here we introduce three-terminal memtransistors based on two-dimensional (2D) materials, which can emulate both probabilistic synapses as well as reconfigurable neurons. The cycle-to-cycle variation in the programming of the 2D memtransistor is exploited to achieve Gaussian random number generator-based synapses, whereas 2D memtransistor based integrated circuits are used to obtain neurons with hyperbolic tangent and sigmoid activation functions. Finally, memtransistor-based synapses and neurons are combined in a crossbar array architecture to realize a BNN accelerator for a data classification task.
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Affiliation(s)
- Amritanand Sebastian
- grid.29857.310000 0001 2097 4281Deparment of Engineering Science and Mechanics, Penn State University, University Park, PA 16802 USA
| | - Rahul Pendurthi
- grid.29857.310000 0001 2097 4281Deparment of Engineering Science and Mechanics, Penn State University, University Park, PA 16802 USA
| | - Azimkhan Kozhakhmetov
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA
| | - Nicholas Trainor
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 42812D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, PA 16802 USA
| | - Joshua A. Robinson
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Chemistry, Penn State University, University Park, PA USA ,grid.29857.310000 0001 2097 4281Department of Physics, Penn State University, University Park, PA USA
| | - Joan M. Redwing
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 42812D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Electrical Engineering and Computer Science, Penn State University, University Park, PA USA
| | - Saptarshi Das
- grid.29857.310000 0001 2097 4281Deparment of Engineering Science and Mechanics, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Electrical Engineering and Computer Science, Penn State University, University Park, PA USA
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21
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Lee T, Jeon SB, Kim D. A Vertical Single Transistor Neuron with Core-Shell Dual-Gate for Excitatory-Inhibitory Function and Tunable Firing Threshold Voltage. MICROMACHINES 2022; 13:1740. [PMID: 36296091 PMCID: PMC9609599 DOI: 10.3390/mi13101740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/10/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
A novel inhibitable and firing threshold voltage tunable vertical nanowire (NW) single transistor neuron device with core-shell dual-gate (CSDG) was realized and verified by TCAD simulation. The CSDG NW neuron is enclosed by an independently accessed shell gate and core gate to serve an excitatory-inhibitory transition and a firing threshold voltage adjustment, respectively. By utilizing the shell gate, the firing of specific neuron can be inhibited for winner-takes-all learning. It was confirmed that the independently accessed core gate can be used for adjustment of the firing threshold voltage to compensate random conductance variation before the learning and to fix inference error caused by unwanted synapse conductance change after the learning. This threshold voltage tuning can also be utilized for homeostatic function during the learning process. Furthermore, a myelination function which controls the transmission rate was obtained based on the inherent asymmetry between the source and drain in vertical NW structure. Finally, using the CSDG NW neuron device, a letter recognition test was conducted by SPICE simulation for a system-level validation. This multi-functional neuron device can contribute to construct a high-density monolithic SNN hardware combining with the previously developed vertical synapse MOSFET devices.
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Affiliation(s)
- Taegoon Lee
- Department of Electronic Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin 17104, Korea
| | - Seung-Bae Jeon
- Department of Electronic Engineering, Hanbat National University, 125 Dongseo-daero, Yuseong-gu, Daejeon 34158, Korea
| | - Daewon Kim
- Department of Electronic Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin 17104, Korea
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22
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Kim SY, Yu JM, Lee GS, Yun DH, Kim MS, Kim JK, Kim DJ, Lee GB, Kim MS, Han JK, Seo M, Choi YK. Synaptic Segmented Transistor with Improved Linearity by Schottky Junctions and Accelerated Speed by Double-Layered Nitride. ACS APPLIED MATERIALS & INTERFACES 2022; 14:32261-32269. [PMID: 35797493 DOI: 10.1021/acsami.2c07975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Neuromorphic devices have been extensively studied to overcome the limitations of a von Neumann system for artificial intelligence. A synaptic device is one of the most important components in the hardware integration for a neuromorphic system because a number of synaptic devices can be connected to a neuron with compactness as high as possible. Therefore, synaptic devices using silicon-based memory, which are advantageous for a high packing density and mass production due to matured fabrication technologies, have attracted considerable attention. In this study, a segmented transistor devoted to an artificial synapse is proposed for the first time to improve the linearity of the potentiation and depression (P/D). It is a complementary metal oxide semiconductor (CMOS)-compatible device that harnesses both non-ohmic Schottky junctions of the source and drain for improved weight linearity and double-layered nitride for enhanced speed. It shows three distinct and unique segments in drain current-gate voltage transfer characteristics induced by Schottky junctions. In addition, the different stoichiometries of SixNy for a double-layered nitride is utilized as a charge trap layer for boosting the operation speed. This work can bring the industry potentially one step closer to realizing the mass production of hardware-based synaptic devices in the future.
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Affiliation(s)
- Seong-Yeon Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
- SK Hynix Inc., Icheon 17336, Republic of Korea
| | - Ji-Man Yu
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Gi Sung Lee
- National Nanofab Center (NNFC), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Dae-Hwan Yun
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
- SK Hynix Inc., Icheon 17336, Republic of Korea
| | - Moon-Seok Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Jin-Ki Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Da-Jin Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Geon-Beom Lee
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Myung-Su Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Joon-Kyu Han
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Myungsoo Seo
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Yang-Kyu Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
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23
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Han JK, Park SC, Yu JM, Ahn JH, Choi YK. A Bioinspired Artificial Gustatory Neuron for a Neuromorphic Based Electronic Tongue. NANO LETTERS 2022; 22:5244-5251. [PMID: 35737524 DOI: 10.1021/acs.nanolett.2c01107] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A novel biomimicked neuromorphic sensor for an energy efficient and highly scalable electronic tongue (E-tongue) is demonstrated with a metal-oxide-semiconductor field-effect transistor (MOSFET). By mimicking a biological gustatory neuron, the proposed E-tongue can simultaneously detect ion concentrations of chemicals on an extended gate and encode spike signals on the MOSFET, which acts as an input neuron in a spiking neural network (SNN). Such in-sensor neuromorphic functioning can reduce the energy and area consumption of the conventional E-tongue hardware. pH-sensitive and sodium-sensitive artificial gustatory neurons are implemented by using two different sensing materials: Al2O3 for pH sensing and sodium ionophore X for sodium ion sensing. In addition, a sensitivity control function inspired by the biological sensory neuron is demonstrated. After the unit device characterization of the artificial gustatory neuron, a fully hardware-based E-tongue that can classify two distinct liquids is demonstrated to show a practical application of the artificial gustatory neurons.
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Affiliation(s)
- Joon-Kyu Han
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Sang-Chan Park
- Department of Electronics Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
| | - Ji-Man Yu
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Jae-Hyuk Ahn
- Department of Electronics Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
| | - Yang-Kyu Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
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24
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Han J, Kang M, Jeong J, Cho I, Yu J, Yoon K, Park I, Choi Y. Artificial Olfactory Neuron for an In-Sensor Neuromorphic Nose. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2106017. [PMID: 35426489 PMCID: PMC9218653 DOI: 10.1002/advs.202106017] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 03/10/2022] [Indexed: 06/02/2023]
Abstract
A neuromorphic module of an electronic nose (E-nose) is demonstrated by hybridizing a chemoresistive gas sensor made of a semiconductor metal oxide (SMO) and a single transistor neuron (1T-neuron) made of a metal-oxide-semiconductor field-effect transistor (MOSFET). By mimicking a biological olfactory neuron, it simultaneously detects a gas and encoded spike signals for in-sensor neuromorphic functioning. It identifies an odor source by analyzing the complicated mixed signals using a spiking neural network (SNN). The proposed E-nose does not require conversion circuits, which are essential for processing the sensory signals between the sensor array and processors in the conventional bulky E-nose. In addition, they do not have to include a central processing unit (CPU) and memory, which are required for von Neumann computing. The spike transmission of the biological olfactory system, which is known to be the main factor for reducing power consumption, is realized with the SNN for power savings compared to the conventional E-nose with a deep neural network (DNN). Therefore, the proposed neuromorphic E-nose is promising for application to Internet of Things (IoT), which demands a highly scalable and energy-efficient system. As a practical example, it is employed as an electronic sommelier by classifying different types of wines.
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Affiliation(s)
- Joon‐Kyu Han
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Mingu Kang
- Department of Mechanical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Jaeseok Jeong
- Department of Mechanical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Incheol Cho
- Department of Mechanical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Ji‐Man Yu
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Kuk‐Jin Yoon
- Department of Mechanical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Inkyu Park
- Department of Mechanical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Yang‐Kyu Choi
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
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25
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Feldhoff F, Toepfer H, Harczos T, Klefenz F. Periodicity Pitch Perception Part III: Sensibility and Pachinko Volatility. Front Neurosci 2022; 16:736642. [PMID: 35356050 PMCID: PMC8959216 DOI: 10.3389/fnins.2022.736642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 02/07/2022] [Indexed: 11/29/2022] Open
Abstract
Neuromorphic computer models are used to explain sensory perceptions. Auditory models generate cochleagrams, which resemble the spike distributions in the auditory nerve. Neuron ensembles along the auditory pathway transform sensory inputs step by step and at the end pitch is represented in auditory categorical spaces. In two previous articles in the series on periodicity pitch perception an extended auditory model had been successfully used for explaining periodicity pitch proved for various musical instrument generated tones and sung vowels. In this third part in the series the focus is on octopus cells as they are central sensitivity elements in auditory cognition processes. A powerful numerical model had been devised, in which auditory nerve fibers (ANFs) spike events are the inputs, triggering the impulse responses of the octopus cells. Efficient algorithms are developed and demonstrated to explain the behavior of octopus cells with a focus on a simple event-based hardware implementation of a layer of octopus neurons. The main finding is, that an octopus' cell model in a local receptive field fine-tunes to a specific trajectory by a spike-timing-dependent plasticity (STDP) learning rule with synaptic pre-activation and the dendritic back-propagating signal as post condition. Successful learning explains away the teacher and there is thus no need for a temporally precise control of plasticity that distinguishes between learning and retrieval phases. Pitch learning is cascaded: At first octopus cells respond individually by self-adjustment to specific trajectories in their local receptive fields, then unions of octopus cells are collectively learned for pitch discrimination. Pitch estimation by inter-spike intervals is shown exemplary using two input scenarios: a simple sinus tone and a sung vowel. The model evaluation indicates an improvement in pitch estimation on a fixed time-scale.
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Affiliation(s)
- Frank Feldhoff
- Advanced Electromagnetics Group, Technische Universität Ilmenau, Ilmenau, Germany
| | - Hannes Toepfer
- Advanced Electromagnetics Group, Technische Universität Ilmenau, Ilmenau, Germany
| | - Tamas Harczos
- Fraunhofer-Institut für Digitale Medientechnologie, Ilmenau, Germany
- Auditory Neuroscience and Optogenetics Laboratory, German Primate Center, Göttingen, Germany
- audifon GmbH & Co. KG, Kölleda, Germany
| | - Frank Klefenz
- Fraunhofer-Institut für Digitale Medientechnologie, Ilmenau, Germany
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26
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Han J, Tcho I, Jeon S, Yu J, Kim W, Choi Y. Self-Powered Artificial Mechanoreceptor Based on Triboelectrification for a Neuromorphic Tactile System. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2105076. [PMID: 35032113 PMCID: PMC8948587 DOI: 10.1002/advs.202105076] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/01/2021] [Indexed: 05/19/2023]
Abstract
A self-powered artificial mechanoreceptor module is demonstrated with a triboelectric nanogenerator (TENG) as a pressure sensor with sustainable energy harvesting and a biristor as a neuron. By mimicking a biological mechanoreceptor, it simultaneously detects the pressure and encodes spike signals to act as an input neuron of a spiking neural network (SNN). A self-powered neuromorphic tactile system composed of artificial mechanoreceptor modules with an energy harvester can greatly reduce the power consumption compared to the conventional tactile system based on von Neumann computing, as the artificial mechanoreceptor module itself does not demand an external energy source and information is transmitted with spikes in a SNN. In addition, the system can detect low pressures near 3 kPa due to the high output range of the TENG. It therefore can be advantageously applied to robotics, prosthetics, and medical and healthcare devices, which demand low energy consumption and low-pressure detection levels. For practical applications of the neuromorphic tactile system, classification of handwritten digits is demonstrated with a software-based simulation. Furthermore, a fully hardware-based breath-monitoring system is implemented using artificial mechanoreceptor modules capable of detecting wind pressure of exhalation in the case of pulmonary respiration and bending pressure in the case of abdominal breathing.
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Affiliation(s)
- Joon‐Kyu Han
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Il‐Woong Tcho
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Seung‐Bae Jeon
- Electronics Engineering DepartmentHanbat National University125 Dongseo‐daero, Yuseong‐guDaejeon34158Republic of Korea
| | - Ji‐Man Yu
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Weon‐Guk Kim
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Yang‐Kyu Choi
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
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27
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Han JK, Oh J, Yu JM, Choi SY, Choi YK. A Vertical Silicon Nanowire Based Single Transistor Neuron with Excitatory, Inhibitory, and Myelination Functions for Highly Scalable Neuromorphic Hardware. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2103775. [PMID: 34605173 DOI: 10.1002/smll.202103775] [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: 06/28/2021] [Revised: 08/25/2021] [Indexed: 06/13/2023]
Abstract
A single transistor neuron (1T-neuron) is demonstrated by using a vertically protruded nanowire from an 8 in. silicon (Si) wafer. The 1T-neuron adopts a gate-all-around structure to completely surround the Si nanowire (Si-NW) to make a floating body and allow aggressive downscaling. The Si-NW is composed of an n+ drain at the top, n+ source at the bottom, and p-type floating body at the middle, which are self-aligned vertically. Thus, it occupies a small footprint area. The gate controls an excitatory/inhibitory function. In addition, myelination of a biological neuron that changes membrane capacitance is mimicked by an inherently asymmetric source/drain structure. Two spiking frequencies at the same input current are controlled by whether the neuron is myelinated or unmyelinated. Using the vertical 1T-neuron, pattern recognition is demonstrated with both measurements and semiempirical circuit simulations. Furthermore, handwritten numbers in the MNIST database are recognized with accuracy of 93% by software-based simulations. Applicability of the vertical 1T-neuron to various neural networks is verified, including a single-layer perceptron, multilayer perceptron, and spiking neural network.
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Affiliation(s)
- Joon-Kyu Han
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, Yuseong-gu, 34141, Republic of Korea
| | - Jungyeop Oh
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, Yuseong-gu, 34141, Republic of Korea
| | - Ji-Man Yu
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, Yuseong-gu, 34141, Republic of Korea
| | - Sung-Yool Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, Yuseong-gu, 34141, Republic of Korea
| | - Yang-Kyu Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, Yuseong-gu, 34141, Republic of Korea
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