Reconfigurable optoelectronic transistors for multimodal recognition

Pengzhan Li, Mingzhen Zhang, Qingli Zhou, Qinghua Zhang, Donggang Xie, Ge Li, Zhuohui Liu, Zheng Wang, Erjia Guo, Meng He, Can Wang, Lin Gu, Guozhen Yang, Kuijuan Jin () and Chen Ge ()
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Pengzhan Li: Chinese Academy of Sciences
Mingzhen Zhang: Chinese Academy of Sciences
Qingli Zhou: Capital Normal University
Qinghua Zhang: Chinese Academy of Sciences
Donggang Xie: Chinese Academy of Sciences
Ge Li: Chinese Academy of Sciences
Zhuohui Liu: Chinese Academy of Sciences
Zheng Wang: Chinese Academy of Sciences
Erjia Guo: Chinese Academy of Sciences
Meng He: Chinese Academy of Sciences
Can Wang: Chinese Academy of Sciences
Lin Gu: Tsinghua University
Guozhen Yang: Chinese Academy of Sciences
Kuijuan Jin: Chinese Academy of Sciences
Chen Ge: Chinese Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Biological nervous system outperforms in both dynamic and static information perception due to their capability to integrate the sensing, memory and processing functions. Reconfigurable neuromorphic transistors, which can be used to emulate different types of biological analogues in a single device, are important for creating compact and efficient neuromorphic computing networks, but their design remains challenging due to the need for opposing physical mechanisms to achieve different functions. Here we report a neuromorphic electrolyte-gated transistor that can be reconfigured to perform physical reservoir and synaptic functions. The device exhibits dynamics with tunable time-scales under optical and electrical stimuli. The nonlinear volatile property is suitable for reservoir computing, which can be used for multimodal pre-processing. The nonvolatility and programmability of the device through ion insertion/extraction achieved via electrolyte gating, which are required to realize synaptic functions, are verified. The device’s superior performance in mimicking human perception of dynamic and static multisensory information based on the reconfigurable neuromorphic functions is also demonstrated. The present study provides an exciting paradigm for the realization of multimodal reconfigurable devices and opens an avenue for mimicking biological multisensory fusion.

Date: 2024
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DOI: 10.1038/s41467-024-47580-2

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