Bioinspired Target Detection Pipeline Based on Two-Dimensional Optoelectronic van der Waals Heterostructures

作者：Yongtao Ding, Yuekun Yang, Hao Hao, Zhuan Li, Chen Pan, Pengfei Wang, Cong Wang, Huibin Tan, Ruochun Jin, Jun Zhang, Shiying Du, Shi‐Jun Liang, Feng Miao, Wen Yang · 发表于：ACS Nano · 年份：2025 · DOI：10.1021/acsnano.5c05791 · 被引用次数：9 · 研究领域：Advanced biosensing and bioanalysis techniques、Nanowire Synthesis and Applications、2D Materials and Applications

Noncooperative target detection in real-world scenarios relies on large-scale deep neural networks after image capture. However, directly implementing this detection pipeline under conventional optoelectronic sensors and computing units leads to physical bottlenecks in latency and energy consumption. Here, inspired by the biological visual attention mechanism and leveraging fabricated two-dimensional optoelectronic van der Waals heterostructure devices, we present a highly efficient neuromorphic in-sensor target detection pipeline. The inherent physical process of infrared self-driven visible photoresponse in heterostructures is used to simplify the originally complex processing in artificial intelligence (AI) optical detection algorithms. Specifically, the high-cost target localization and image fusion process can be directly implemented in the sensing unit. This manipulation decreases redundant information at the sensor level, reducing the burden on data transmission and backend computation. The results show that our bioinspired pipeline achieves a mean average precision (mAP) of 95.85% in detecting real-world scenes, even in extreme environments. Meanwhile, significant reductions in computing load (31.65%), latency (95.66%), and energy consumption (21.25%) can be attained compared with previous research. Our work provides a scalable material-for-AI solution for real-time, highly efficient target detection applications in real-world settings.