Cheng Jie, Ouyang Xinyu, Tang Xin, Qin Bingdong, Liu Shu, Chen Hu, Song Bing, Zheng Yu
The State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University, Changsha 410073, China.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China.
ACS Appl Mater Interfaces. 2025 Apr 30;17(17):25467-25477. doi: 10.1021/acsami.5c01496. Epub 2025 Apr 16.
Recently, the growing demand for data-centric applications has significantly accelerated progress to overcome the "memory wall" caused by the separation of image sensing, memory, and computing units. However, despite advancements in novel devices driving the development of the in-sensor computing paradigm, achieving seamless integration of optical sensing, storage, and image processing within a single device remains challenging. This study demonstrates an in-sensor computing architecture using a ferroelectric-defined reconfigurable α-InSe phototransistor. The three polarization states of the device exhibit a linear and distinguishable photoresponse, with a maximum photoresponse current difference of 2.17 × 10 A and a retention time exceeding 500 s. The nonvolatile weight and synaptic properties are programmed by external electrical stimulation, enabling 112 distinct conductance states with a nonlinearity of 0.12. Additionally, the device supports efficient optical writing, electrical erasing, optoelectronic logic, and decoding via combined optoelectronic control. In-sensor computation for image edge detection is simulated by embedding a nonvolatile Prewitt convolution kernel into a 3 × 3 device array. The integrated structure and array design highlight the strong potential of 2D ferroelectric semiconductors for in-sensor computing, providing a promising platform for next-generation multifunctional artificial vision systems.
最近，对以数据为中心的应用程序不断增长的需求显著加速了克服由图像传感、内存和计算单元分离所导致的“内存墙”的进程。然而，尽管新型器件的进步推动了传感器内计算范式的发展，但在单个器件内实现光学传感、存储和图像处理的无缝集成仍然具有挑战性。本研究展示了一种使用铁电定义的可重构α-InSe光电晶体管的传感器内计算架构。该器件的三种极化状态呈现出线性且可区分的光响应，最大光响应电流差为2.17×10 A，保持时间超过500秒。非易失性权重和突触特性通过外部电刺激进行编程，可实现112种不同的电导状态，非线性度为0.12。此外，该器件通过组合光电控制支持高效的光学写入、电擦除、光电逻辑和解码。通过将非易失性Prewitt卷积核嵌入3×3器件阵列中，模拟了用于图像边缘检测的传感器内计算。这种集成结构和阵列设计突出了二维铁电半导体在传感器内计算方面的强大潜力，为下一代多功能人工视觉系统提供了一个有前景的平台。
