Floating gate photo-memory devices based on van der Waals heterostructures for neuromorphic image recognition
Muhammad Zubair, Yi Dong, Bin Cai, Xiao Fu, Hailu Wang, Tangxin Li, Jinjin Wang, S. Liu, Mengjia Xia, Qixiao Zhao, Runzhang Xie, Hangyu Xu, Xiaoyong Jiang, Shuhong Hu, Bo Song, Xiaolong Chen, Jiadong Zhou, Lixin Dong, Jinshui Miao
Abstract
Two-dimensional (2D) materials with reconfigurable properties show potential in neuromorphic hardware applications. However, most 2D materials-based neuromorphic hardware is volatile, which needs large energy to accomplish perception functions. Here, we report on nonvolatile floating gate photo-memory devices based on ReS2/h-BN/SnS2 van der Waals heterostructures. The devices exhibit a large memory window of ∼60 V, a high program/erase current ratio of ∼107 with excellent retention characteristics, a low off-state current of 7.4 × 10−13 A, and a high detectivity of 1.98 × 1013 cm Hz1/2 W−1, allowing for multi-bit information storage. For the multi-level storage capacity, 27 photo-memory states are obtained by pulsed laser illumination. Moreover, a neuromorphic computing network is also constructed based on the photo-memory devices with a maximum recognition accuracy of up to 90%. This work paves the way for miniaturization and high-density integration of future optoelectronics for neuromorphic hardware applications.