Zhao Tu, Yue Wenbo, Deng Qunrui, Chen Wenjie, Luo Chengming, Zhou Yao, Sun Meng, Li Xueming, Yang Yujue, Huo Nengjie
Guangdong Provincial Key Laboratory of Chip and Integration Technology, School of Electronic Science and Engineering (School of Microelectronics), South China Normal University, Foshan, 528225, P. R. China.
School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, 510006, P. R. China.
Adv Mater. 2025 Jul;37(27):e2419208. doi: 10.1002/adma.202419208. Epub 2025 Apr 15.
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), a strong photo-gating effect enables highly sensitive photo-response with responsivity of ≈6.515 kA W and detectivity up to ≈3.92 × 10 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. At zero or positive V, 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.
在商业人工视觉系统(AVS)中,传感、存储和计算单元由于其架构和性能差距通常在物理上是分离的,这从而增加了体积、复杂性和能量损耗。这项工作开发了一种将这些不同模块集成在单个器件内的神经形态晶体管。利用栅极可调的面外电场,该器件实现了光电传感器、光存储器和视觉突触的多模式集成。当在负顶栅电压(V)下工作时,强光电门控效应实现了高灵敏度的光响应,响应率约为6.515 kA/W,探测率高达约3.92×10 Jones。由于电荷存储效应,它还可以作为一种非易失性多电平(>4位)光存储器,具有超过10000 s的长耐久性和高达10的高写入/擦除比。在零或正V时,晶体管切换到具有神经形态计算能力的视觉突触模式,为复杂的生物学习和灵活的突触可塑性提供了一条途径。通过进一步将突触可塑性与人工神经网络(ANN)相结合,它实现了高达95.26%的精确图像识别和分类。这项工作开发了一种集成AVS关键组件的多模式晶体管,解决了一体化集成和制造复杂性方面的现有挑战。