A Dynamic Memory for Reservoir Computing Utilizing Ion Migration in CuInP2S6

Time-series analysis and forecasting play a vital role in the fields of economics and engineering. Neuromorphic computing, particularly recurrent neural networks (RNNs), has emerged as an effective approach to address these tasks. Reservoir computing (RC), a type of RNN, offers a powerful and efficient solution for handling nonlinear information in high-dimensional spaces and addressing temporal tasks. CuInP2S6 (CIPS), a van der Waals material with ion conductivity, shows promise for sequential task processing. Here, a synapse device based on CIPS is demonstrated that exhibits temporal dynamics under electrical stimulation. By controlling Cu+ ion migration, this study successfully emulates synaptic performance, including potentiation and depression characteristics, and RC. Migration of Cu+ ions is confirmed using piezoresponse and Kelvin probe force microscopy. The device achieves low normalized root mean square errors (NRMSE) of 0.04762 and 0.01402 for the Henon map and Mackey-Glass series tasks, respectively. For real-life time-series prediction based on the Jena temperature database, an overall NRMSE of 0.03339 is achieved. These results highlight the potential of CIPS ion conductivity for real-time signal processing in machine learning, expanding applications in neuromorphic computing.

Automated summary

Neuromorphic computing, specifically reservoir computing (RC), is an effective approach for time-series analysis and forecasting in economics and engineering. In this study, a synapse device based on CuInP2S6 (CIPS) material is demonstrated, achieving synaptic performance emulation and temporal dynamics under electrical stimulation. The migration of Cu+ ions in CIPS is controlled, and the device exhibits low normalized root mean square errors (NRMSE) for various tasks, highlighting the potential of CIPS for real-time signal processing and expanding applications in neuromorphic computing.