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Pan Z, Zhang J, Liu X, Zhao L, Ma J, Luo C, Sun Y, Dan Z, Gao W, Lu X, Li J, Huo N. Thermally Oxidized Memristor and 1T1R Integration for Selector Function and Low-Power Memory. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2401915. [PMID: 38958519 DOI: 10.1002/advs.202401915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 06/02/2024] [Indexed: 07/04/2024]
Abstract
Resistive switching memories have garnered significant attention due to their high-density integration and rapid in-memory computing beyond von Neumann's architecture. However, significant challenges are posed in practical applications with respect to their manufacturing process complexity, a leakage current of high resistance state (HRS), and the sneak-path current problem that limits their scalability. Here, a mild-temperature thermal oxidation technique for the fabrication of low-power and ultra-steep memristor based on Ag/TiOx/SnOx/SnSe2/Au architecture is developed. Benefiting from a self-assembled oxidation layer and the formation/rupture of oxygen vacancy conductive filaments, the device exhibits an exceptional threshold switching behavior with high switch ratio exceeding 106, low threshold voltage of ≈1 V, long-term retention of >104 s, an ultra-small subthreshold swing of 2.5 mV decade-1 and high air-stability surpassing 4 months. By decreasing temperature, the device undergoes a transition from unipolar volatile to bipolar nonvolatile characteristics, elucidating the role of oxygen vacancies migration on the resistive switching process. Further, the 1T1R structure is established between a memristor and a 2H-MoTe2 transistor by the van der Waals (vdW) stacking approach, achieving the functionality of selector and multi-value memory with lower power consumption. This work provides a mild-thermal oxidation technology for the low-cost production of high-performance memristors toward future in-memory computing applications.
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Affiliation(s)
- Zhidong Pan
- School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P. R. China
| | - Jielian Zhang
- School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P. R. China
| | - Xueting Liu
- School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P. R. China
| | - Lei Zhao
- School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P. R. China
| | - Jingyi Ma
- School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P. R. China
| | - Chunlai Luo
- School of South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, 510006, P. R. China
| | - Yiming Sun
- School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P. R. China
| | - Zhiying Dan
- School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P. R. China
| | - Wei Gao
- School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P. R. China
| | - Xubing Lu
- School of South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, 510006, P. R. China
| | - Jingbo Li
- College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Nengjie Huo
- School of Semiconductor Science and Technology, South China Normal University, Foshan, 528225, P. R. China
- Guangdong Provincial Key Laboratory of Chip and Integration Technology, Guangzhou, 510631, P. R. China
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Lee J, Woo G, Cho J, Son S, Shin H, Seok H, Kim MJ, Kim E, Wang Z, Kang B, Jang WJ, Kim T. Free-standing two-dimensional ferro-ionic memristor. Nat Commun 2024; 15:5162. [PMID: 38890313 PMCID: PMC11189491 DOI: 10.1038/s41467-024-48810-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 05/15/2024] [Indexed: 06/20/2024] Open
Abstract
Two-dimensional (2D) ferroelectric materials have emerged as significant platforms for multi-functional three-dimensional (3D) integrated electronic devices. Among 2D ferroelectric materials, ferro-ionic CuInP2S6 has the potential to achieve the versatile advances in neuromorphic computing systems due to its phase tunability and ferro-ionic characteristics. As CuInP2S6 exhibits a ferroelectric phase with insulating properties at room temperature, the external temperature and electrical field should be required to activate the ferro-ionic conduction. Nevertheless, such external conditions inevitably facilitate stochastic ionic conduction, which completely limits the practical applications of 2D ferro-ionic materials. Herein, free-standing 2D ferroelectric heterostructure is mechanically manipulated for nano-confined conductive filaments growth in free-standing 2D ferro-ionic memristor. The ultra-high mechanical bending is selectively facilitated at the free-standing area to spatially activate the ferro-ionic conduction, which allows the deterministic local positioning of Cu+ ion transport. According to the local flexoelectric engineering, 5.76×102-fold increased maximum current is observed within vertical shear strain 720 nN, which is theoretically supported by the 3D flexoelectric simulation. In conclusion, we envision that our universal free-standing platform can provide the extendable geometric solution for ultra-efficient self-powered system and reliable neuromorphic device.
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Affiliation(s)
- Jinhyoung Lee
- School of Mechanical Engineering, Sungkyunkwan University (SKKU), Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
- Center for Quantum Nanoscience, Institute for Basic Science (IBS), Seoul, 03760, Republic of Korea.
| | - Gunhoo Woo
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
| | - Jinill Cho
- School of Mechanical Engineering, Sungkyunkwan University (SKKU), Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Sihoon Son
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Hyelim Shin
- Department of Semiconductor Convergence Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Hyunho Seok
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Min-Jae Kim
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Eungchul Kim
- AVP Process Development Team, Samsung Electronics, Chungcheongnam-do, Cheonan-si, 31086, Republic of Korea
| | - Ziyang Wang
- School of Mechanical Engineering, Sungkyunkwan University (SKKU), Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Boseok Kang
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
- Department of Nano Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Won-Jun Jang
- Center for Quantum Nanoscience, Institute for Basic Science (IBS), Seoul, 03760, Republic of Korea
- Department of Physics, Ewha Womans University, Seoul, 03760, Republic of Korea
| | - Taesung Kim
- School of Mechanical Engineering, Sungkyunkwan University (SKKU), Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
- Department of Semiconductor Convergence Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
- Department of Nano Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
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Xu M, Chen X, Guo Y, Wang Y, Qiu D, Du X, Cui Y, Wang X, Xiong J. Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301063. [PMID: 37285592 DOI: 10.1002/adma.202301063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/15/2023] [Indexed: 06/09/2023]
Abstract
Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.
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Affiliation(s)
- Minyi Xu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinrui Chen
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yehao Guo
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yang Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dong Qiu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinchuan Du
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yi Cui
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jie Xiong
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
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Jiang M, Shan K, He C, Li C. Efficient combinatorial optimization by quantum-inspired parallel annealing in analogue memristor crossbar. Nat Commun 2023; 14:5927. [PMID: 37739944 PMCID: PMC10516914 DOI: 10.1038/s41467-023-41647-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/11/2023] [Indexed: 09/24/2023] Open
Abstract
Combinatorial optimization problems are prevalent in various fields, but obtaining exact solutions remains challenging due to the combinatorial explosion with increasing problem size. Special-purpose hardware such as Ising machines, particularly memristor-based analog Ising machines, have emerged as promising solutions. However, existing simulate-annealing-based implementations have not fully exploited the inherent parallelism and analog storage/processing features of memristor crossbar arrays. This work proposes a quantum-inspired parallel annealing method that enables full parallelism and improves solution quality, resulting in significant speed and energy improvement when implemented in analog memristor crossbars. We experimentally solved tasks, including unweighted and weighted Max-Cut and traveling salesman problem, using our integrated memristor chip. The quantum-inspired parallel annealing method implemented in memristor-based hardware has demonstrated significant improvements in time- and energy-efficiency compared to previously reported simulated annealing and Ising machine implemented on other technologies. This is because our approach effectively exploits the natural parallelism, analog conductance states, and all-to-all connection provided by memristor technology, promising its potential for solving complex optimization problems with greater efficiency.
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Affiliation(s)
- Mingrui Jiang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Keyi Shan
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Chengping He
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Can Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
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Liu H, Qin Y, Chen HY, Wu J, Ma J, Du Z, Wang N, Zou J, Lin S, Zhang X, Zhang Y, Wang H. Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205047. [PMID: 36609920 DOI: 10.1002/adma.202205047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.
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Affiliation(s)
- Hefei Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Yuan Qin
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Hung-Yu Chen
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiangbin Wu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiahui Ma
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Zhonghao Du
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Nan Wang
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jingyi Zou
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Sen Lin
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Xu Zhang
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Yuhao Zhang
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Han Wang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
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