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Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing.
Wu, Guangjian; Zhang, Xumeng; Feng, Guangdi; Wang, Jingli; Zhou, Keji; Zeng, Jinhua; Dong, Danian; Zhu, Fangduo; Yang, Chenkai; Zhao, Xiaoming; Gong, Danni; Zhang, Mengru; Tian, Bobo; Duan, Chungang; Liu, Qi; Wang, Jianlu; Chu, Junhao; Liu, Ming.
  • Wu G; State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China.
  • Zhang X; Shanghai Qi Zhi Institute, Xuhui District, Shanghai, China.
  • Feng G; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
  • Wang J; State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China.
  • Zhou K; Shanghai Qi Zhi Institute, Xuhui District, Shanghai, China.
  • Zeng J; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
  • Dong D; Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China.
  • Zhu F; State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China.
  • Yang C; State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China.
  • Zhao X; State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China.
  • Gong D; Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China.
  • Zhang M; State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China.
  • Tian B; Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China.
  • Duan C; Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China.
  • Liu Q; Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China.
  • Wang J; State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, China.
  • Chu J; Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China. bbtian@ee.ecnu.edu.cn.
  • Liu M; Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, China.
Nat Mater ; 22(12): 1499-1506, 2023 Dec.
Article en En | MEDLINE | ID: mdl-37770677
Recently, the increasing demand for data-centric applications is driving the elimination of image sensing, memory and computing unit interface, thus promising for latency- and energy-strict applications. Although dedicated electronic hardware has inspired the development of in-memory computing and in-sensor computing, folding the entire signal chain into one device remains challenging. Here an in-memory sensing and computing architecture is demonstrated using ferroelectric-defined reconfigurable two-dimensional photodiode arrays. High-level cognitive computing is realized based on the multiplications of light power and photoresponsivity through the photocurrent generation process and Kirchhoff's law. The weight is stored and programmed locally by the ferroelectric domains, enabling 51 (>5 bit) distinguishable weight states with linear, symmetric and reversible manipulation characteristics. Image recognition can be performed without any external memory and computing units. The three-in-one paradigm, integrating high-level computing, weight memorization and high-performance sensing, paves the way for a computing architecture with low energy consumption, low latency and reduced hardware overhead.

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2023 Tipo del documento: Article