神经形态工程学
光探测
材料科学
光电子学
异质结
宽带
计算机科学
半导体
光电探测器
突触重量
人工神经网络
电子工程
电场
接口(物质)
等离子体子
水准点(测量)
人工智能
电压
纳米技术
还原(数学)
图像传感器
载流子
光子学
卷积神经网络
作者
G. R. Zhou,Jiyuan Xu,Zimeng Wang,Xiaohui Jiao,Keyi Wang,Nan Xiao,Yucheng Zhu,Nitin Mallik,Azzedine Bendounan,Zailan Zhang,Haibo Zeng,Zhesheng Chen
出处
期刊:ACS Nano
[American Chemical Society]
日期:2026-09-10
标识
DOI:10.1021/acsnano.6c05081
摘要
Abstract Two-dimensional (2D) semiconductors exhibit significant potential for next-generation optoelectronic and neuromorphic applications. In particular, intrinsic defect states in 2D materials not only modulate the doping level but also play key roles in the photoresponse time of optoelectronic devices. Depending on the role of the defect states, either high-speed photodetection or low-speed defect-mediated synaptic functions can be realized within individual device. However, the integration of these two functions into a single device remains a challenge. Here, we report a dual-mode optoelectronic device based on an n-ReS2/p-PdSe2 heterojunction, enabling bias-switchable operation between self-driven photodetection and synaptic functions. At zero bias, the strong built-in electric field enables self-driven broadband detection with fast response times of 22.3 μs/22.8 μs. Under an applied bias, intrinsic defects and interfacial traps at the 2D material/substrate interface act as charge-trapping centers to emulate synaptic behaviors. Furthermore, the device emulates the “perception-memory-processing” capabilities of an artificial visual system and exhibits polarization-sensitive neuromorphic responses for spatial-domain image processing. Combined with artificial neural networks, it achieves efficient image denoising and contrast enhancement, reducing training epochs for high-precision handwritten digit recognition by 70%. This work presents a universal strategy for integrating high-speed detection and synaptic functions in multifunctional optoelectronics, showing promise for next-generation artificial vision and neuromorphic computing.
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