2D materials–based homogeneous transistor-memory architecture for neuromorphic hardware
Future artificial intelligence applications and data-intensive computations require the development of neuromorphic systems beyond traditional heterogeneous device architectures. Physical separation between a peripheral signal-processing unit and a memory-operating unit is one of the main bottleneck...
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Published in | Science (American Association for the Advancement of Science) Vol. 373; no. 6561; pp. 1353 - 1358 |
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Main Authors | , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
The American Association for the Advancement of Science
17.09.2021
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Subjects | |
Online Access | Get full text |
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Abstract | Future artificial intelligence applications and data-intensive computations require the development of neuromorphic systems beyond traditional heterogeneous device architectures. Physical separation between a peripheral signal-processing unit and a memory-operating unit is one of the main bottlenecks of heterogeneous architectures, blocking further improvements in efficient resistance matching, energy consumption, and integration compatibility. Tong
et al
. present a transistor-memory architecture based on a homogeneous tungsten selenide-on-lithium niobate device array (see the Perspective by Rao and Tao). Analog peripheral signal preprocessing and nonvolatile memory were possible within the same device structure, promising diverse neuromorphic functionalities and offering potential improvements in neuromorphic systems on-chip. —YS
Homogeneous integration of 2D WSe
2
(as a peripheral circuit) on LiNbO
3
(as a memory array) can improve neuromorphic architectures.
In neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus, exploration of homogeneous devices for these components is key for improving module integration and resistance matching. Inspired by the ferroelectric proximity effect on two-dimensional (2D) materials, we present a tungsten diselenide–on–lithium niobate cascaded architecture as a basic device that functions as a nonlinear transistor, assisting the design of operational amplifiers for analog signal processing (ASP). This device also functions as a nonvolatile memory cell, achieving memory operating (MO) functionality. On the basis of this homogeneous architecture, we also investigated an ASP-MO integrated system for binary classification and the design of ternary content-addressable memory for potential use in neuromorphic hardware. |
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AbstractList | In neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus, exploration of homogeneous devices for these components is key for improving module integration and resistance matching. Inspired by the ferroelectric proximity effect on two-dimensional (2D) materials, we present a tungsten diselenide–on–lithium niobate cascaded architecture as a basic device that functions as a nonlinear transistor, assisting the design of operational amplifiers for analog signal processing (ASP). This device also functions as a nonvolatile memory cell, achieving memory operating (MO) functionality. On the basis of this homogeneous architecture, we also investigated an ASP-MO integrated system for binary classification and the design of ternary content-addressable memory for potential use in neuromorphic hardware.In neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus, exploration of homogeneous devices for these components is key for improving module integration and resistance matching. Inspired by the ferroelectric proximity effect on two-dimensional (2D) materials, we present a tungsten diselenide–on–lithium niobate cascaded architecture as a basic device that functions as a nonlinear transistor, assisting the design of operational amplifiers for analog signal processing (ASP). This device also functions as a nonvolatile memory cell, achieving memory operating (MO) functionality. On the basis of this homogeneous architecture, we also investigated an ASP-MO integrated system for binary classification and the design of ternary content-addressable memory for potential use in neuromorphic hardware. Future artificial intelligence applications and data-intensive computations require the development of neuromorphic systems beyond traditional heterogeneous device architectures. Physical separation between a peripheral signal-processing unit and a memory-operating unit is one of the main bottlenecks of heterogeneous architectures, blocking further improvements in efficient resistance matching, energy consumption, and integration compatibility. Tong et al . present a transistor-memory architecture based on a homogeneous tungsten selenide-on-lithium niobate device array (see the Perspective by Rao and Tao). Analog peripheral signal preprocessing and nonvolatile memory were possible within the same device structure, promising diverse neuromorphic functionalities and offering potential improvements in neuromorphic systems on-chip. —YS Homogeneous integration of 2D WSe 2 (as a peripheral circuit) on LiNbO 3 (as a memory array) can improve neuromorphic architectures. In neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus, exploration of homogeneous devices for these components is key for improving module integration and resistance matching. Inspired by the ferroelectric proximity effect on two-dimensional (2D) materials, we present a tungsten diselenide–on–lithium niobate cascaded architecture as a basic device that functions as a nonlinear transistor, assisting the design of operational amplifiers for analog signal processing (ASP). This device also functions as a nonvolatile memory cell, achieving memory operating (MO) functionality. On the basis of this homogeneous architecture, we also investigated an ASP-MO integrated system for binary classification and the design of ternary content-addressable memory for potential use in neuromorphic hardware. Memory and logic in the same deviceFuture artificial intelligence applications and data-intensive computations require the development of neuromorphic systems beyond traditional heterogeneous device architectures. Physical separation between a peripheral signal-processing unit and a memory-operating unit is one of the main bottlenecks of heterogeneous architectures, blocking further improvements in efficient resistance matching, energy consumption, and integration compatibility. Tong et al. present a transistor-memory architecture based on a homogeneous tungsten selenide-on-lithium niobate device array (see the Perspective by Rao and Tao). Analog peripheral signal preprocessing and nonvolatile memory were possible within the same device structure, promising diverse neuromorphic functionalities and offering potential improvements in neuromorphic systems on-chip. —YSIn neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus, exploration of homogeneous devices for these components is key for improving module integration and resistance matching. Inspired by the ferroelectric proximity effect on two-dimensional (2D) materials, we present a tungsten diselenide–on–lithium niobate cascaded architecture as a basic device that functions as a nonlinear transistor, assisting the design of operational amplifiers for analog signal processing (ASP). This device also functions as a nonvolatile memory cell, achieving memory operating (MO) functionality. On the basis of this homogeneous architecture, we also investigated an ASP-MO integrated system for binary classification and the design of ternary content-addressable memory for potential use in neuromorphic hardware. In neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus, exploration of homogeneous devices for these components is key for improving module integration and resistance matching. Inspired by the ferroelectric proximity effect on two-dimensional (2D) materials, we present a tungsten diselenide–on–lithium niobate cascaded architecture as a basic device that functions as a nonlinear transistor, assisting the design of operational amplifiers for analog signal processing (ASP). This device also functions as a nonvolatile memory cell, achieving memory operating (MO) functionality. On the basis of this homogeneous architecture, we also investigated an ASP-MO integrated system for binary classification and the design of ternary content-addressable memory for potential use in neuromorphic hardware. |
Author | Miao, Xiangshui Li, Zheng Wang, Peng Tong, Lei Wang, Yilun Peng, Zhuiri Xia, Hui Xiong, Wei Ye, Lei Xue, Kan-Hao Xu, Jianbin Zhang, Xinliang Xu, Hangyu Hu, Weida Liu, Feng Lin, Runfeng Xu, Mingsheng Huang, Xinyu |
Author_xml | – sequence: 1 givenname: Lei orcidid: 0000-0001-6254-5215 surname: Tong fullname: Tong, Lei organization: Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China – sequence: 2 givenname: Zhuiri orcidid: 0000-0001-9527-3842 surname: Peng fullname: Peng, Zhuiri organization: Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China – sequence: 3 givenname: Runfeng orcidid: 0000-0003-4938-2903 surname: Lin fullname: Lin, Runfeng organization: Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China – sequence: 4 givenname: Zheng orcidid: 0000-0002-6555-6371 surname: Li fullname: Li, Zheng organization: Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China – sequence: 5 givenname: Yilun orcidid: 0000-0001-9416-2219 surname: Wang fullname: Wang, Yilun organization: Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China – sequence: 6 givenname: Xinyu orcidid: 0000-0002-3449-1907 surname: Huang fullname: Huang, Xinyu organization: Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China – sequence: 7 givenname: Kan-Hao orcidid: 0000-0002-2894-7912 surname: Xue fullname: Xue, Kan-Hao organization: Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China – sequence: 8 givenname: Hangyu orcidid: 0000-0002-8204-161X surname: Xu fullname: Xu, Hangyu organization: State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China – sequence: 9 givenname: Feng orcidid: 0000-0002-3301-7613 surname: Liu fullname: Liu, Feng organization: School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei 430072, China – sequence: 10 givenname: Hui surname: Xia fullname: Xia, Hui organization: State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China – sequence: 11 givenname: Peng orcidid: 0000-0002-7287-7595 surname: Wang fullname: Wang, Peng organization: State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China – sequence: 12 givenname: Mingsheng surname: Xu fullname: Xu, Mingsheng organization: School of Micro-Nano Electronics, State Key Laboratory of Silicon Materials, Zhejiang University, Hangzhou 310027, China – sequence: 13 givenname: Wei orcidid: 0000-0002-2532-2503 surname: Xiong fullname: Xiong, Wei organization: Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China – sequence: 14 givenname: Weida orcidid: 0000-0001-5278-8969 surname: Hu fullname: Hu, Weida organization: State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China – sequence: 15 givenname: Jianbin orcidid: 0000-0003-0509-9508 surname: Xu fullname: Xu, Jianbin organization: Department of Electronic Engineering, Materials Science and Technology Research Center, The Chinese University of Hong Kong, Hong Kong, China – sequence: 16 givenname: Xinliang orcidid: 0000-0001-8513-3328 surname: Zhang fullname: Zhang, Xinliang organization: Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China – sequence: 17 givenname: Lei orcidid: 0000-0001-5195-1867 surname: Ye fullname: Ye, Lei organization: Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China – sequence: 18 givenname: Xiangshui orcidid: 0000-0002-3999-7421 surname: Miao fullname: Miao, Xiangshui organization: Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34413170$$D View this record in MEDLINE/PubMed |
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Snippet | Future artificial intelligence applications and data-intensive computations require the development of neuromorphic systems beyond traditional heterogeneous... In neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus, exploration of homogeneous... Memory and logic in the same deviceFuture artificial intelligence applications and data-intensive computations require the development of neuromorphic systems... |
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