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 effici...

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Bibliographic Details
Published in:Advanced electronic materials 2024-01, Vol.10 (1), p.n/a
Main Authors: Wu, Yangwu, Duong, Ngoc Thanh, Chien, Yu‐Chieh, Liu, Song, Ang, Kah‐Wee
Format: Article
Language:English
Subjects:
2D materials
copper ion conductivity
Crystal structure
CuInP2S6
Electric fields
Electrical resistivity
Ion migration
Machine learning
Neural networks
Neuromorphic computing
Random access memory
Recurrent neural networks
reservoir computing
Scanning electron microscopy
Spectrum analysis
temporal signal tasks
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Summary: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 Hénon 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.
ISSN:2199-160X
2199-160X
DOI:10.1002/aelm.202300481