Neuromorphic Transistors Integrating Photo‐Sensor, Optical Memory and Visual Synapses for Artificial Vision Application

In commercial artificial vision system (AVS), the sensing, storage, and computing units are usually physically separated due to their architecture and performance gaps, which thus increases the volume, complexity, and energy loss. This work develops a neuromorphic transistor integrating these different modules within one single device. Leveraging the gate-tunable out-of-plane electric field, the device achieves the multi-mode integration of photo-sensor, optical memory, and visual synapse. When operating at negative top gate voltage (V<sub>TG</sub>), a strong photo-gating effect enables highly sensitive photo-response with responsivity of ≈6.515 kA W<sup>-1</sup> and detectivity up to ≈3.92 × 10<sup>14</sup> Jones. Due to the charge storage effect, it can also act as a non-volatile multi-level (>4 bits) optical memory with a long endurance of over 10 000 s and a high writing/erasing ratio of up to 10<sup>6</sup>. At zero or positive V<sub>TG</sub>, the transistor switches to visual synapse mode with neuromorphic computing capability, providing a pathway for complex biological learning and flexible synaptic plasticity. By further combining the synaptic plasticity with an artificial neural network (ANN), it achieves precise image recognition and classification with an accuracy of up to 95.26%. This work develops a multi-mode transistor that integrates key components of an AVS, addressing the existing challenges of all-in-one integration and manufacturing complexity.

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Institutions

Publication Details

Journal
Advanced Materials
Published
2025-04-15
DOI
https://doi.org/10.1002/adma.202419208
Citations
38
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
27.76

Funders

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article

Neuromorphic Transistors Integrating Photo‐Sensor, Optical Memory and Visual Synapses for Artificial Vision Application

Qunrui Deng, Wenjie Chen, Yujue Yang, Nengjie Huo et al.
38 citations
Advanced Materials
Advanced Memory and Neural Computing
27.76
article

Neuromorphic Transistors Integrating Photo‐Sensor, Optical Memory and Visual Synapses for Artificial Vision Application

Qunrui Deng, Wenjie Chen, Yujue Yang, Nengjie Huo, Ruhong Zhou, Xueming Li, Cheng‐Ming Luo, Wenbo Yue, Meng Sun, Tu Zhao
article en
38 citations

Abstract

In commercial artificial vision system (AVS), the sensing, storage, and computing units are usually physically separated due to their architecture and performance gaps, which thus increases the volume, complexity, and energy loss. This work develops a neuromorphic transistor integrating these different modules within one single device. Leveraging the gate-tunable out-of-plane electric field, the device achieves the multi-mode integration of photo-sensor, optical memory, and visual synapse. When operating at negative top gate voltage (V<sub>TG</sub>), a strong photo-gating effect enables highly sensitive photo-response with responsivity of ≈6.515 kA W<sup>-1</sup> and detectivity up to ≈3.92 × 10<sup>14</sup> Jones. Due to the charge storage effect, it can also act as a non-volatile multi-level (>4 bits) optical memory with a long endurance of over 10 000 s and a high writing/erasing ratio of up to 10<sup>6</sup>. At zero or positive V<sub>TG</sub>, the transistor switches to visual synapse mode with neuromorphic computing capability, providing a pathway for complex biological learning and flexible synaptic plasticity. By further combining the synaptic plasticity with an artificial neural network (ANN), it achieves precise image recognition and classification with an accuracy of up to 95.26%. This work develops a multi-mode transistor that integrates key components of an AVS, addressing the existing challenges of all-in-one integration and manufacturing complexity.

Advanced MaterialsVol. 37(27)
Guangdong University of Technology (CN), South China Normal University (CN)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Affordable and clean energy
Openalex Percentile: Top 0%
Advanced Memory and Neural Computing
27.76
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