Visualized in-sensor computing

Yao Ni, Jiaqi Liu, Hong Han, Qianbo Yu, Lu Yang, Zhipeng Xu, Chengpeng Jiang, Lu Liu and Wentao Xu ()
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Yao Ni: Nankai University
Jiaqi Liu: Nankai University
Hong Han: Nankai University
Qianbo Yu: Nankai University
Lu Yang: Nankai University
Zhipeng Xu: Nankai University
Chengpeng Jiang: Nankai University
Lu Liu: Nankai University
Wentao Xu: Nankai University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract In artificial nervous systems, conductivity changes indicate synaptic weight updates, but they provide limited information compared to living organisms. We present the pioneering design and production of an electrochromic neuromorphic transistor employing color updates to represent synaptic weight for in-sensor computing. Here, we engineer a specialized mechanism for adaptively regulating ion doping through an ion-exchange membrane, enabling precise control over color-coded synaptic weight, an unprecedented achievement. The electrochromic neuromorphic transistor not only enhances electrochromatic capabilities for hardware coding but also establishes a visualized pattern-recognition network. Integrating the electrochromic neuromorphic transistor with an artificial whisker, we simulate a bionic reflex system inspired by the longicorn beetle, achieving real-time visualization of signal flow within the reflex arc in response to environmental stimuli. This research holds promise in extending the biomimetic coding paradigm and advancing the development of bio-hybrid interfaces, particularly in incorporating color-based expressions.

Date: 2024
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DOI: 10.1038/s41467-024-47630-9

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