In-Sensor Computing: Materials, Devices, and Integration Technologies

被引:132
|
作者
Wan, Tianqing [1 ]
Shao, Bangjie [1 ]
Ma, Sijie [1 ]
Zhou, Yue [1 ]
Li, Qiao [1 ]
Chai, Yang [1 ,2 ]
机构
[1] Hong Kong Polytech Univ, Dept Appl Phys, Hong Kong, Peoples R China
[2] Hong Kong Polytech Univ, Shenzhen Res Inst, Shenzhen, Peoples R China
关键词
bioinspired devices; in-sensor computing; near-sensor computing; optoelectronic devices; vision sensors; SUPERLINEAR PHOTOCONDUCTIVITY; SYNAPTIC TRANSISTOR; ATOMICALLY THIN; PHOTONICS; VISION; MEMORY;
D O I
10.1002/adma.202203830
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in-sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in-sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in-sensor computing is provided.
引用
收藏
页数:14
相关论文
共 50 条
  • [21] Halide perovskite photovoltaics for in-sensor reservoir computing
    Sharma, Divyam
    Luqman, Alka
    Ng, Si En
    Yantara, Natalia
    Xing, Xuechao
    Tay, Yeow Boon
    Basu, Arindam
    Chattopadhyay, Anupam
    Mathews, Nripan
    NANO ENERGY, 2024, 129
  • [22] Multimode modulated memristors for in-sensor computing system
    Zhang Yu-Qi
    Wang Jun-Jie
    Lu Zi-Yu
    Han Su-Ting
    ACTA PHYSICA SINICA, 2022, 71 (14)
  • [23] A Photomemristor With Temporal Dynamics for In-Sensor Reservoir Computing
    Cai, Bingqi
    Wang, Tianyu
    Wang, Chen
    Sun, Qingqing
    Zhang, David Wei
    Chen, Lin
    IEEE ELECTRON DEVICE LETTERS, 2024, 45 (04) : 570 - 573
  • [24] In-sensor dynamic computing for intelligent machine vision
    Yang, Yuekun
    Pan, Chen
    Li, Yixiang
    Yangdong, Xingjian
    Wang, Pengfei
    Li, Zhu-An
    Wang, Shuang
    Yu, Wentao
    Liu, Guanyu
    Cheng, Bin
    Di, Zengfeng
    Liang, Shi-Jun
    Miao, Feng
    NATURE ELECTRONICS, 2024, 7 (03) : 225 - 233
  • [25] In-sensor optoelectronic computing using electrostatically doped silicon
    Jang, Houk
    Hinton, Henry
    Jung, Woo-Bin
    Lee, Min-Hyun
    Kim, Changhyun
    Park, Min
    Lee, Seoung-Ki
    Park, Seongjun
    Ham, Donhee
    NATURE ELECTRONICS, 2022, 5 (08) : 519 - 525
  • [26] Energy efficient artificial gustatory system for in-sensor computing
    Khanday, Mudasir A.
    Rashid, Shazia
    Khanday, Farooq A.
    MICRO AND NANOSTRUCTURES, 2024, 191
  • [27] Analog Circuit Implementation of Neural Networks for In-Sensor Computing
    Zhu, Jianghan
    Chen, Bingzhen
    Yang, Zhitao
    Meng, Lingxiao
    Ye, Terry Tao
    2021 IEEE COMPUTER SOCIETY ANNUAL SYMPOSIUM ON VLSI (ISVLSI 2021), 2021, : 150 - 156
  • [28] MXene-ZnO Memristor for Multimodal In-Sensor Computing
    Wang, Yan
    Gong, Yue
    Yang, Lin
    Xiong, Ziyu
    Lv, Ziyu
    Xing, Xuechao
    Zhou, Ye
    Zhang, Bing
    Su, Chenliang
    Liao, Qiufan
    Han, Su-Ting
    ADVANCED FUNCTIONAL MATERIALS, 2021, 31 (21)
  • [29] In-sensor optoelectronic computing using electrostatically doped silicon
    Houk Jang
    Henry Hinton
    Woo-Bin Jung
    Min-Hyun Lee
    Changhyun Kim
    Min Park
    Seoung-Ki Lee
    Seongjun Park
    Donhee Ham
    Nature Electronics, 2022, 5 : 519 - 525
  • [30] Neural networks based on in-sensor computing of optoelectronic memristor
    Zhang, Zhang
    Wang, Qifan
    Shi, Gang
    Ma, Yongbo
    Zeng, Jianmin
    Liu, Gang
    MICROELECTRONIC ENGINEERING, 2024, 291