Reservoir Computing Enabled by Polymer Electrolyte-Gated MoS Transistors for Time-Series Processing.

This study presented a novel reservoir computing (RC) system based on polymer electrolyte-gated MoS transistors. The proposed transistors operate through lithium ion (Li) intercalation, which induces reversible phase transitions between semiconducting 2H and metallic 1T' phases in MoS films. This mechanism enables dynamic conductance modulation with inherent nonlinearity and fading memory effects, rendering these transistors particularly suitable as reservoir nodes. Our RC implementation leverages time-multiplexed virtual nodes to reduce physical component requirements while maintaining rich temporal dynamics. Testing on a spoken digit recognition task using the NIST TI-46 dataset demonstrated 95.1% accuracy, while chaotic time-series prediction of the Lorenz system achieved a normalized root mean square error as low as 0.04. This work established polymer electrolyte-gated MoS transistors as promising building blocks for efficient RC systems capable of processing complex temporal patterns, offering enhanced scalability, and practical applicability in neuromorphic computation.