A Robust Biomimetic van der Waals Heterostructure Visual Neuromorphic Device for Multiscale In-Sensor Reservoir Computing
Yinxing Zhang, Gongjie Liu, Yuzhe Zhang, Yiming Yuan, Zhipeng Xu, Qiuhong Li, Chuancheng Jia, Wentao Xu · ACS Nano · 2025
Visual neuromorphic devices are critical for processing exponentially increasing complex visual information due to their capability in capturing, storing, and processing optical signals from the environment. In particular, all-two-dimensional material heterostructure-based visual neuromorphic devices are recognized as one of the most promising device architectures for mitigating lattice mismatch and interfacial defects inherent in conventional heterostructures, owing to their atomic-scale interfacial compatibility. Herein, we report a robust visual neuromorphic device based on graphdiyne/MoS 2 all-two-dimensional material heterostructure. By utilizing the unique sp-sp 2 hybridization and abundant two-dimensionally distributed charge carrier interaction sites provided by acetylenic bonds in graphdiyne films, we achieved nearly a 10-fold enhancement in the memory window of the device. The device exhibits an ultrahigh on/off ratio of 5 × 10 7, cycling endurance of 70 cycles, and 4 weeks air stability. Most importantly, a multiscale in-sensor reservoir computing system was developed, demonstrating over 90% recognition accuracy on facial data sets under 5%–40% Gaussian noise conditions. Its tunable relaxation time characteristic enables efficient motion trajectory recognition with an accuracy of 95.46%. A proof-of-concept demonstration confirms that the device achieves trajectory recognition within a temporal resolution range of 10 2 –10 6 ms. This work presents a promising device codesign approach that advances the development of neuromorphic vision systems.