1
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Yang DP, Tang XG, Sun QJ, Chen JY, Jiang YP, Zhang D, Dong HF. Emerging ferroelectric materials ScAlN: applications and prospects in memristors. MATERIALS HORIZONS 2024; 11:2802-2819. [PMID: 38525789 DOI: 10.1039/d3mh01942j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
The research found that after doping with rare earth elements, a large number of electrons and holes will be produced on the surface of AlN, which makes the material have the characteristics of spontaneous polarization. A new type of ferroelectric material has made a new breakthrough in the application of nitride-materials in the field of integrated devices. In this paper, the application prospects and development trends of ferroelectric material ScAlN in memristors are reviewed. Firstly, various fabrication processes and structures of the current ScAlN thin films are described in detail to explore the implementation of their applications in synaptic devices. Secondly, a series of electrical properties of ScAlN films, such as the current switching ratio and long-term cycle durability, were tested to explore whether their electrical properties could meet the basic needs of memristor device materials. Finally, a series of summaries on the current research studies of ScAlN thin films in the synaptic simulation are made, and the working state of ScAlN thin films as a synaptic device is observed. The results show that the ScAlN ferroelectric material has high residual polarization, no wake-up function, excellent stability and obvious STDP behavior, which indicates that the modified material has wide application prospects in the research and development of memristors.
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Affiliation(s)
- Dong-Ping Yang
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.
| | - Xin-Gui Tang
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.
| | - Qi-Jun Sun
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.
| | - Jia-Ying Chen
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.
| | - Yan-Ping Jiang
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.
| | - Dan Zhang
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.
| | - Hua-Feng Dong
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.
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Jayakrishnan AR, Kim JS, Hellenbrand M, Marques LS, MacManus-Driscoll JL, Silva JPB. Growth of emergent simple pseudo-binary ferroelectrics and their potential in neuromorphic computing devices. MATERIALS HORIZONS 2024; 11:2355-2371. [PMID: 38477152 PMCID: PMC11104485 DOI: 10.1039/d4mh00153b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
Ferroelectric memory devices such as ferroelectric memristors, ferroelectric tunnel junctions, and field-effect transistors are considered among the most promising candidates for neuromorphic computing devices. The promise arises from their defect-independent switching mechanism, low energy consumption and high power efficiency, and important properties being aimed for are reliable switching at high speed, excellent endurance, retention, and compatibility with complementary metal-oxide-semiconductor (CMOS) technology. Binary or doped binary materials have emerged over conventional complex-composition ferroelectrics as an optimum solution, particularly in terms of CMOS compatibility. The current state-of-the-art route to achieving superlative ferroelectric performance of binary oxides is to induce ferroelectricity at the nanoscale, e.g., in ultra-thin films of doped HfO2, ZrO2, Zn1-xMgxO, Al-xScxN, and Bi1-xSmxO3. This short review article focuses on the materials science of emerging new ferroelectric materials, including their different properties such as remanent polarization, coercive field, endurance, etc. The potential of these materials is discussed for neuromorphic applications.
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Affiliation(s)
- Ampattu R Jayakrishnan
- Physics Center of Minho and Porto Universities (CF-UM-UP), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
- Laboratory of Physics for Materials and Emergent Technologies, LapMET, University of Minho, 4710-057 Braga, Portugal
| | - Ji S Kim
- Dept. of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Rd., Cambridge, CB3 OFS, UK.
| | - Markus Hellenbrand
- Dept. of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Rd., Cambridge, CB3 OFS, UK.
| | - Luís S Marques
- Physics Center of Minho and Porto Universities (CF-UM-UP), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
- Laboratory of Physics for Materials and Emergent Technologies, LapMET, University of Minho, 4710-057 Braga, Portugal
| | - Judith L MacManus-Driscoll
- Dept. of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Rd., Cambridge, CB3 OFS, UK.
| | - José P B Silva
- Physics Center of Minho and Porto Universities (CF-UM-UP), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
- Laboratory of Physics for Materials and Emergent Technologies, LapMET, University of Minho, 4710-057 Braga, Portugal
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3
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Liu Y, Wang T, Xu K, Li Z, Yu J, Meng J, Zhu H, Sun Q, Zhang DW, Chen L. Low-power and high-speed HfLaO-based FE-TFTs for artificial synapse and reconfigurable logic applications. MATERIALS HORIZONS 2024; 11:490-498. [PMID: 37966103 DOI: 10.1039/d3mh01461d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Emulating the human nervous system to build next-generation computing architectures is considered a promising way to solve the von Neumann bottleneck. Transistors based on ferroelectric layers are strong contenders for the basic unit of artificial neural systems due to their advantages of high speed and low power consumption. In this work, the potential of Fe-TFTs integrating the HfLaO ferroelectric film and ultra-thin ITO channel for artificial synaptic devices is demonstrated for the first time. The Fe-TFTs can respond significantly to pulses as low as 14 ns with an energy consumption of 93.1 aJ, which is at the leading level for similar devices. In addition, Fe-TFTs exhibit essential synaptic functions and achieve a recognition rate of 93.2% for handwritten digits. Notably, a novel reconfigurable approach involving the combination of two types of electrical pulses to realize Boolean logic operations ("AND", "OR") within a single Fe-TFT has been introduced for the first time. The simulations of array-level operations further demonstrated the potential for parallel computing. These multifunctional Fe-TFTs reveal new hardware options for neuromorphic computing chips.
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Affiliation(s)
- Yongkai Liu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Tianyu Wang
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Kangli Xu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Zhenhai Li
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jiajie Yu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jialin Meng
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qingqing Sun
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Lin Chen
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
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Luo J, Tian G, Zhang DG, Zhang XC, Lu ZN, Zhang ZD, Cai JW, Zhong YN, Xu JL, Gao X, Wang SD. Voltage-Mode Ferroelectric Synapse for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2023; 15:48452-48461. [PMID: 37802499 DOI: 10.1021/acsami.3c09506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Ferroelectric materials with a modulable polarization extent hold promise for exploring voltage-driven neuromorphic hardware, in which direct current flow can be minimized. Utilizing a single active layer of an insulating ferroelectric polymer, we developed a voltage-mode ferroelectric synapse that can continuously and reversibly update its states. The device states are straightforwardly manifested in the form of variable output voltage, enabling large-scale direct cascading of multiple ferroelectric synapses to build a deep physical neural network. Such a neural network based on potential superposition rather than current flow is analogous to the biological counterpart driven by action potentials in the brain. A high accuracy of over 97% for the simulation of handwritten digit recognition is achieved using the voltage-mode neural network. The controlled ferroelectric polarization, revealed by piezoresponse force microscopy, turns out to be responsible for the synaptic weight updates in the ferroelectric synapses. The present work demonstrates an alternative strategy for the design and construction of emerging artificial neural networks.
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Affiliation(s)
- Jie Luo
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Guo Tian
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, P. R. China
| | - Ding-Guo Zhang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Xing-Chen Zhang
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhen-Ni Lu
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Zhong-Da Zhang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Jia-Wei Cai
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Ya-Nan Zhong
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Jian-Long Xu
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Xu Gao
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Sui-Dong Wang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
- Macao Institute of Materials Science and Engineering (MIMSE), MUST-SUDA Joint Research Center for Advanced Functional Materials, Macau University of Science and Technology, Taipa, Macao 999078, P. R. China
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5
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Park JY, Choe DH, Lee DH, Yu GT, Yang K, Kim SH, Park GH, Nam SG, Lee HJ, Jo S, Kuh BJ, Ha D, Kim Y, Heo J, Park MH. Revival of Ferroelectric Memories Based on Emerging Fluorite-Structured Ferroelectrics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204904. [PMID: 35952355 DOI: 10.1002/adma.202204904] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Over the last few decades, the research on ferroelectric memories has been limited due to their dimensional scalability and incompatibility with complementary metal-oxide-semiconductor (CMOS) technology. The discovery of ferroelectricity in fluorite-structured oxides revived interest in the research on ferroelectric memories, by inducing nanoscale nonvolatility in state-of-the-art gate insulators by minute doping and thermal treatment. The potential of this approach has been demonstrated by the fabrication of sub-30 nm electronic devices. Nonetheless, to realize practical applications, various technical limitations, such as insufficient reliability including endurance, retention, and imprint, as well as large device-to-device-variation, require urgent solutions. Furthermore, such limitations should be considered based on targeting devices as well as applications. Various types of ferroelectric memories including ferroelectric random-access-memory, ferroelectric field-effect-transistor, and ferroelectric tunnel junction should be considered for classical nonvolatile memories as well as emerging neuromorphic computing and processing-in-memory. Therefore, from the viewpoint of materials science, this review covers the recent research focusing on ferroelectric memories from the history of conventional approaches to future prospects.
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Affiliation(s)
- Ju Yong Park
- Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Duk-Hyun Choe
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Dong Hyun Lee
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Geun Taek Yu
- School of Materials Science and Engineering, Pusan National University, Busan, 46241, Republic of Korea
| | - Kun Yang
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Se Hyun Kim
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Geun Hyeong Park
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Seung-Geol Nam
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Hyun Jae Lee
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Sanghyun Jo
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Bong Jin Kuh
- Semiconductor Research and Development Center, Samsung Electronics, Hwaseong, 18448, Republic of Korea
| | - Daewon Ha
- Semiconductor Research and Development Center, Samsung Electronics, Hwaseong, 18448, Republic of Korea
| | - Yongsung Kim
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Jinseong Heo
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Min Hyuk Park
- Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
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Nikam RD, Lee J, Lee K, Hwang H. Exploring the Cutting-Edge Frontiers of Electrochemical Random Access Memories (ECRAMs) for Neuromorphic Computing: Revolutionary Advances in Material-to-Device Engineering. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2302593. [PMID: 37300356 DOI: 10.1002/smll.202302593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/23/2023] [Indexed: 06/12/2023]
Abstract
Advanced materials and device engineering has played a crucial role in improving the performance of electrochemical random access memory (ECRAM) devices. ECRAM technology has been identified as a promising candidate for implementing artificial synapses in neuromorphic computing systems due to its ability to store analog values and its ease of programmability. ECRAM devices consist of an electrolyte and a channel material sandwiched between two electrodes, and the performance of these devices depends on the properties of the materials used. This review provides a comprehensive overview of material engineering strategies to optimize the electrolyte and channel materials' ionic conductivity, stability, and ionic diffusivity to improve the performance and reliability of ECRAM devices. Device engineering and scaling strategies are further discussed to enhance ECRAM performance. Last, perspectives on the current challenges and future directions in developing ECRAM-based artificial synapses in neuromorphic computing systems are provided.
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Affiliation(s)
- Revannath Dnyandeo Nikam
- Center for Single Atom-based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Jongwon Lee
- Center for Single Atom-based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Kyumin Lee
- Center for Single Atom-based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Hyunsang Hwang
- Center for Single Atom-based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
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Mikolajick T, Park MH, Begon-Lours L, Slesazeck S. From Ferroelectric Material Optimization to Neuromorphic Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2206042. [PMID: 36017895 DOI: 10.1002/adma.202206042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/11/2022] [Indexed: 06/15/2023]
Abstract
Due to the voltage driven switching at low voltages combined with nonvolatility of the achieved polarization state, ferroelectric materials have a unique potential for low power nonvolatile electronic devices. The competitivity of such devices is hindered by compatibility issues of well-known ferroelectrics with established semiconductor technology. The discovery of ferroelectricity in hafnium oxide changed this situation. The natural application of nonvolatile devices is as a memory cell. Nonvolatile memory devices also built the basis for other applications like in-memory or neuromorphic computing. Three different basic ferroelectric devices can be constructed: ferroelectric capacitors, ferroelectric field effect transistors and ferroelectric tunneling junctions. In this article first the material science of the ferroelectricity in hafnium oxide will be summarized with a special focus on tailoring the switching characteristics towards different applications.The current status of nonvolatile ferroelectric memories then lays the ground for looking into applications like in-memory computing. Finally, a special focus will be given to showcase how the basic building blocks of spiking neural networks, the neuron and the synapse, can be realized and how they can be combined to realize neuromorphic computing systems. A summary, comparison with other technologies like resistive switching devices and an outlook completes the paper.
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Affiliation(s)
- Thomas Mikolajick
- NaMLab gGmbH, Noethnitzer Strasse 64 a, 01187, Dresden, Germany
- Institute of Semiconductors and Microsystems, TU Dresden, 01069, Dresden, Germany
| | - Min Hyuk Park
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, Republic of Korea
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8
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Lee HC, Kim J, Kim HR, Kim KH, Park KJ, So JP, Lee JM, Hwang MS, Park HG. Nanograin network memory with reconfigurable percolation paths for synaptic interactions. LIGHT, SCIENCE & APPLICATIONS 2023; 12:118. [PMID: 37188669 DOI: 10.1038/s41377-023-01168-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 04/15/2023] [Accepted: 04/23/2023] [Indexed: 05/17/2023]
Abstract
The development of memory devices with functions that simultaneously process and store data is required for efficient computation. To achieve this, artificial synaptic devices have been proposed because they can construct hybrid networks with biological neurons and perform neuromorphic computation. However, irreversible aging of these electrical devices causes unavoidable performance degradation. Although several photonic approaches to controlling currents have been suggested, suppression of current levels and switching of analog conductance in a simple photonic manner remain challenging. Here, we demonstrated a nanograin network memory using reconfigurable percolation paths in a single Si nanowire with solid core/porous shell and pure solid core segments. The electrical and photonic control of current percolation paths enabled the analog and reversible adjustment of the persistent current level, exhibiting memory behavior and current suppression in this single nanowire device. In addition, the synaptic behaviors of memory and erasure were demonstrated through potentiation and habituation processes. Photonic habituation was achieved using laser illumination on the porous nanowire shell, with a linear decrease in the postsynaptic current. Furthermore, synaptic elimination was emulated using two adjacent devices interconnected on a single nanowire. Therefore, electrical and photonic reconfiguration of the conductive paths in Si nanograin networks will pave the way for next-generation nanodevice technologies.
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Affiliation(s)
- Hoo-Cheol Lee
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Jungkil Kim
- Department of Physics, Jeju National University, Jeju, 63243, Republic of Korea.
| | - Ha-Reem Kim
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Kyoung-Ho Kim
- Department of Physics, Chungbuk National University, Cheongju, 28644, Republic of Korea
| | - Kyung-Jun Park
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Jae-Pil So
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Jung Min Lee
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Min-Soo Hwang
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea
| | - Hong-Gyu Park
- Department of Physics, Korea University, Seoul, 02841, Republic of Korea.
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9
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Li C, Zhang X, Chen P, Zhou K, Yu J, Wu G, Xiang D, Jiang H, Wang M, Liu Q. Short-term synaptic plasticity in emerging devices for neuromorphic computing. iScience 2023; 26:106315. [PMID: 36950108 PMCID: PMC10025973 DOI: 10.1016/j.isci.2023.106315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Abstract
Neuromorphic computing is a promising computing paradigm toward building next-generation artificial intelligence machines, in which diverse types of synaptic plasticity play an active role in information processing. Compared to long-term plasticity (LTP) forming the foundation of learning and memory, short-term plasticity (STP) is essential for critical computational functions. So far, the practical applications of LTP have been widely investigated, whereas the implementation of STP in hardware is still elusive. Here, we review the development of STP by bridging the physics in emerging devices and biological behaviors. We explore the computational functions of various STP in biology and review their recent progress. Finally, we discuss the main challenges of introducing STP into synaptic devices and offer the potential approaches to utilize STP to enrich systems' capabilities. This review is expected to provide prospective ideas for implementing STP in emerging devices and may promote the construction of high-level neuromorphic machines.
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Affiliation(s)
- Chao Li
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xumeng Zhang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Pei Chen
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
| | - Keji Zhou
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Jie Yu
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
| | - Guangjian Wu
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Du Xiang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Hao Jiang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Ming Wang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Qi Liu
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
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10
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Wang P, Wang D, Mondal S, Hu M, Wu Y, Ma T, Mi Z. Ferroelectric Nitride Heterostructures on CMOS Compatible Molybdenum for Synaptic Memristors. ACS APPLIED MATERIALS & INTERFACES 2023; 15:18022-18031. [PMID: 36975150 DOI: 10.1021/acsami.2c22798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Achieving ferroelectricity in III-nitride (III-N) semiconductors by alloying with rare-earth elements, e.g., scandium, has presented a pivotal step toward next-generation electronic, acoustic, photonic, and quantum devices and systems. To date, however, the conventional growth of single-crystalline nitride semiconductors often requires the use of sapphire, Si, or SiC substrate, which has prevented their integration with the workhorse complementary metal oxide semiconductor (CMOS) technology. Herein, we demonstrate single-crystalline ferroelectric nitride semiconductors grown on CMOS compatible metal-molybdenum. Significantly, we find that a unique epitaxial relationship between wurtzite and body-centered cubic crystal structure can be well maintained, enabling the realization of single-crystalline wurtzite ferroelectric nitride semiconductors on polycrystalline molybdenum that was not previously possible. Robust and wake-up-free ferroelectricity has been measured, for the first time, in the epitaxially grown ScAlN directly on metal. We further propose and demonstrate a ferroelectric GaN/ScAlN heterostructure for synaptic memristor, which shows the capability of emulating the spike-time-dependent plasticity in a biological synapse. This work provides a viable path for the integration of III-N architectures with the mature CMOS technology and sheds light on the promising applications of ferroelectric nitride memristors in neuromorphic computing.
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Affiliation(s)
- Ping Wang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ding Wang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Shubham Mondal
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Mingtao Hu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Yuanpeng Wu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Tao Ma
- Michigan Center for Materials Characterization, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Zetian Mi
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
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Soliman M, Maity K, Gloppe A, Mahmoudi A, Ouerghi A, Doudin B, Kundys B, Dayen JF. Photoferroelectric All-van-der-Waals Heterostructure for Multimode Neuromorphic Ferroelectric Transistors. ACS APPLIED MATERIALS & INTERFACES 2023; 15:15732-15744. [PMID: 36919904 PMCID: PMC10375436 DOI: 10.1021/acsami.3c00092] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Interface-driven effects in ferroelectric van der Waals (vdW) heterostructures provide fresh opportunities in the search for alternative device architectures toward overcoming the von Neumann bottleneck. However, their implementation is still in its infancy, mostly by electrical control. It is of utmost interest to develop strategies for additional optical and multistate control in the quest for novel neuromorphic architectures. Here, we demonstrate the electrical and optical control of the ferroelectric polarization states of ferroelectric field effect transistors (FeFET). The FeFETs, fully made of ReS2/hBN/CuInP2S6 vdW materials, achieve an on/off ratio exceeding 107, a hysteresis memory window up to 7 V wide, and multiple remanent states with a lifetime exceeding 103 s. Moreover, the ferroelectric polarization of the CuInP2S6 (CIPS) layer can be controlled by photoexciting the vdW heterostructure. We perform wavelength-dependent studies, which allow for identifying two mechanisms at play in the optical control of the polarization: band-to-band photocarrier generation into the 2D semiconductor ReS2 and photovoltaic voltage into the 2D ferroelectric CIPS. Finally, heterosynaptic plasticity is demonstrated by operating our FeFET in three different synaptic modes: electrically stimulated, optically stimulated, and optically assisted synapse. Key synaptic functionalities are emulated including electrical long-term plasticity, optoelectrical plasticity, optical potentiation, and spike rate-dependent plasticity. The simulated artificial neural networks demonstrate an excellent accuracy level of 91% close to ideal-model synapses. These results provide a fresh background for future research on photoferroelectric vdW systems and put ferroelectric vdW heterostructures on the roadmap for the next neuromorphic computing architectures.
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Affiliation(s)
- Mohamed Soliman
- Université de Strasbourg, CNRS, Institut de Physique et Chimie des Matériaux de Strasbourg (IPCMS), UMR 7504, 23 rue du Loess, Strasbourg 67034, France
| | - Krishna Maity
- Université de Strasbourg, CNRS, Institut de Physique et Chimie des Matériaux de Strasbourg (IPCMS), UMR 7504, 23 rue du Loess, Strasbourg 67034, France
| | - Arnaud Gloppe
- Université de Strasbourg, CNRS, Institut de Physique et Chimie des Matériaux de Strasbourg (IPCMS), UMR 7504, 23 rue du Loess, Strasbourg 67034, France
| | - Aymen Mahmoudi
- CNRS, Centre de Nanosciences et de Nanotechnologies, Université Paris-Saclay, 91120 Palaiseau, France
| | - Abdelkarim Ouerghi
- CNRS, Centre de Nanosciences et de Nanotechnologies, Université Paris-Saclay, 91120 Palaiseau, France
| | - Bernard Doudin
- Université de Strasbourg, CNRS, Institut de Physique et Chimie des Matériaux de Strasbourg (IPCMS), UMR 7504, 23 rue du Loess, Strasbourg 67034, France
- Institut Universitaire de France (IUF), 1 rue Descartes, 75231 cedex 05 Paris, France
| | - Bohdan Kundys
- Université de Strasbourg, CNRS, Institut de Physique et Chimie des Matériaux de Strasbourg (IPCMS), UMR 7504, 23 rue du Loess, Strasbourg 67034, France
| | - Jean-Francois Dayen
- Université de Strasbourg, CNRS, Institut de Physique et Chimie des Matériaux de Strasbourg (IPCMS), UMR 7504, 23 rue du Loess, Strasbourg 67034, France
- Institut Universitaire de France (IUF), 1 rue Descartes, 75231 cedex 05 Paris, France
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12
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Halter M, Bégon-Lours L, Sousa M, Popoff Y, Drechsler U, Bragaglia V, Offrein BJ. A multi-timescale synaptic weight based on ferroelectric hafnium zirconium oxide. COMMUNICATIONS MATERIALS 2023; 4:14. [PMID: 36843629 PMCID: PMC9936949 DOI: 10.1038/s43246-023-00342-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Brain-inspired computing emerged as a forefront technology to harness the growing amount of data generated in an increasingly connected society. The complex dynamics involving short- and long-term memory are key to the undisputed performance of biological neural networks. Here, we report on sub-µm-sized artificial synaptic weights exploiting a combination of a ferroelectric space charge effect and oxidation state modulation in the oxide channel of a ferroelectric field effect transistor. They lead to a quasi-continuous resistance tuning of the synapse by a factor of 60 and a fine-grained weight update of more than 200 resistance values. We leverage a fast, saturating ferroelectric effect and a slow, ionic drift and diffusion process to engineer a multi-timescale artificial synapse. Our device demonstrates an endurance of more than 10 10 cycles, a ferroelectric retention of more than 10 years, and various types of volatility behavior on distinct timescales, making it well suited for neuromorphic and cognitive computing.
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Affiliation(s)
- Mattia Halter
- IBM Research Europe - Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
- ETH Zurich - Integrated Systems Laboratory, CH-8092 Zurich, Switzerland
- Present Address: Lumiphase AG, CH-8712 Stäfa, Switzerland
| | - Laura Bégon-Lours
- IBM Research Europe - Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
| | - Marilyne Sousa
- IBM Research Europe - Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
| | - Youri Popoff
- IBM Research Europe - Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
- ETH Zurich - Integrated Systems Laboratory, CH-8092 Zurich, Switzerland
| | - Ute Drechsler
- IBM Research Europe - Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
| | - Valeria Bragaglia
- IBM Research Europe - Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
| | - Bert Jan Offrein
- IBM Research Europe - Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
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Zhang F, Li C, Li Z, Dong L, Zhao J. Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications. MICROSYSTEMS & NANOENGINEERING 2023; 9:16. [PMID: 36817330 PMCID: PMC9935897 DOI: 10.1038/s41378-023-00487-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/17/2022] [Accepted: 01/03/2023] [Indexed: 06/18/2023]
Abstract
Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for changes in synaptic strength, enabling the brain to learn from experience. With the rapid development of neuromorphic electronics, tremendous efforts have been devoted to designing and fabricating electronic devices that can mimic synapse operating modes. This growing interest in the field will provide unprecedented opportunities for new hardware architectures for artificial intelligence. In this review, we focus on research of three-terminal artificial synapses based on two-dimensional (2D) materials regulated by electrical, optical and mechanical stimulation. In addition, we systematically summarize artificial synapse applications in various sensory systems, including bioplastic bionics, logical transformation, associative learning, image recognition, and multimodal pattern recognition. Finally, the current challenges and future perspectives involving integration, power consumption and functionality are outlined.
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Affiliation(s)
- Fanqing Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Chunyang Li
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Zhongyi Li
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Lixin Dong
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, 999077 Hong Kong, China
| | - Jing Zhao
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
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14
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Lee DH, Park H, Cho WJ. Synaptic Transistors Based on PVA: Chitosan Biopolymer Blended Electric-Double-Layer with High Ionic Conductivity. Polymers (Basel) 2023; 15:polym15040896. [PMID: 36850180 PMCID: PMC9959983 DOI: 10.3390/polym15040896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/03/2023] [Accepted: 02/07/2023] [Indexed: 02/15/2023] Open
Abstract
This study proposed a biocompatible polymeric organic material-based synaptic transistor gated with a biopolymer electrolyte. A polyvinyl alcohol (PVA):chitosan (CS) biopolymer blended electrolyte with high ionic conductivity was used as an electrical double layer (EDL). It served as a gate insulator with a key function as an artificial synaptic transistor. The frequency-dependent capacitance characteristics of PVA:CS-based biopolymer EDL were evaluated using an EDL capacitor (Al/PVA: CS blended electrolyte-based EDL/Pt configuration). Consequently, the PVA:CS blended electrolyte behaved as an EDL owing to high capacitance (1.53 µF/cm2) at 100 Hz and internal mobile protonic ions. Electronic synaptic transistors fabricated using the PVA:CS blended electrolyte-based EDL membrane demonstrated basic artificial synaptic behaviors such as excitatory post-synaptic current modulation, paired-pulse facilitation, and dynamic signal-filtering functions by pre-synaptic spikes. In addition, the spike-timing-dependent plasticity was evaluated using synaptic spikes. The synaptic weight modulation was stable during repetitive spike cycles for potentiation and depression. Pattern recognition was conducted through a learning simulation for artificial neural networks (ANNs) using Modified National Institute of Standards and Technology datasheets to examine the neuromorphic computing system capability (high recognition rate of 92%). Therefore, the proposed synaptic transistor is suitable for ANNs and shows potential for biological and eco-friendly neuromorphic systems.
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Affiliation(s)
- Dong-Hee Lee
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Hamin Park
- Department of Electronic Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Won-Ju Cho
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
- Correspondence: ; Tel.: +82-2-940-5163
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Kim HS, Park H, Cho WJ. Biocompatible Casein Electrolyte-Based Electric-Double-Layer for Artificial Synaptic Transistors. NANOMATERIALS 2022; 12:nano12152596. [PMID: 35957025 PMCID: PMC9370711 DOI: 10.3390/nano12152596] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/27/2022] [Accepted: 07/27/2022] [Indexed: 02/04/2023]
Abstract
In this study, we proposed a synaptic transistor using an emerging biocompatible organic material, namely, the casein electrolyte as an electric-double-layer (EDL) in the transistor. The frequency-dependent capacitance of the indium-tin-oxide (ITO)/casein electrolyte-based EDL/ITO capacitor was assessed. As a result, the casein electrolyte was identified to exhibit a large capacitance of ~1.74 μF/cm2 at 10 Hz and operate as an EDL owing to the internal proton charge. Subsequently, the implementation of synaptic functions was verified by fabricating the synaptic transistors using biocompatible casein electrolyte-based EDL. The excitatory post-synaptic current, paired-pulse facilitation, and signal-filtering functions of the transistors demonstrated significant synaptic behavior. Additionally, the spike-timing-dependent plasticity was emulated by applying the pre- and post-synaptic spikes to the gate and drain, respectively. Furthermore, the potentiation and depression characteristics modulating the synaptic weight operated stably in repeated cycle tests. Finally, the learning simulation was conducted using the Modified National Institute of Standards and Technology datasets to verify the neuromorphic computing capability; the results indicate a high recognition rate of 90%. Therefore, our results indicate that the casein electrolyte is a promising new EDL material that implements artificial synapses for building environmental and biologically friendly neuromorphic systems.
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Affiliation(s)
- Hwi-Su Kim
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Korea;
| | - Hamin Park
- Department of Electronic Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Korea;
| | - Won-Ju Cho
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Korea;
- Correspondence: ; Tel.: +82-2-940-5163
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16
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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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