High-Performance Physical Reservoir Computing Based on Phase-Change VO 2 Memristor and Explainable Three-Dimensional Collaborative Mapping Mechanism
Song Li, Zewen Li, Linqing Zhou, Fang Wang, Shan Wang, Xin Lin, Shaojie Fan, Yiqi Ma, Xuanyi Wang, Junqing Wei, Yuchan Wang, Hongling Guo, Tianling Ren, Xiaobing Yan, Yan Cheng, Kailiang Zhang · ACS Applied Materials & Interfaces · 2026
Physical reservoir computing (RC) extracts temporal features by utilizing the inherent physical characteristics of materials. Although memristor-based RC systems process time series effectively, they have issues with explainability and compatibility for edge intelligence. In order to go beyond single-mode current/conductance sampling in conventional physical RC, this work makes use of the coexistence of nonlinear event detection and linear exact mapping in phase-change materials to construct a novel spiking RC system. Device-mapped features are processed using an innovatively proposed sliding-window spike sampling architecture and a parameter optimization approach that creates a high-dimensional mapping reservoir by combining a simulated annealing algorithm with a generative adversarial network. This system achieves a low error rate of 0.075 in Mackey-Glass time series prediction and 96.67% accuracy in Iris data set classification. Additionally, this work not only introduces a novel material system into physical RC but also establishes a three-dimensional collaborative mapping mechanism to improve explainability by including a weight-quantification-based explainability analysis method. This method is adaptable to broader material platforms for advancing physical RC development.