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 inScience (American Association for the Advancement of Science) Vol. 373; no. 6561; pp. 1353 - 1358
Main Authors Tong, Lei, Peng, Zhuiri, Lin, Runfeng, Li, Zheng, Wang, Yilun, Huang, Xinyu, Xue, Kan-Hao, Xu, Hangyu, Liu, Feng, Xia, Hui, Wang, Peng, Xu, Mingsheng, Xiong, Wei, Hu, Weida, Xu, Jianbin, Zhang, Xinliang, Ye, Lei, Miao, Xiangshui
Format Journal Article
LanguageEnglish
Published United States The American Association for the Advancement of Science 17.09.2021
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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.
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
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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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SubjectTerms Amplifier design
Architecture
Artificial intelligence
Associative memory
Computer architecture
Energy consumption
Ferroelectricity
Hardware
Integration
Lithium
Lithium niobates
Matching
Memory
Neuromorphic computing
Operational amplifiers
Proximity effect (electricity)
Selenide
Semiconductor devices
Signal processing
Transistors
Tungsten
Two dimensional materials
Title 2D materials–based homogeneous transistor-memory architecture for neuromorphic hardware
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