Reservoir Computing Enabled by Polymer Electrolyte-Gated MoS₂ Transistors for Time-Series Processing

This study presented a novel reservoir computing (RC) system based on polymer electrolyte-gated MoS₂ transistors. The proposed transistors operate through lithium ion (Li⁺) intercalation, which induces reversible phase transitions between semiconducting 2H and metallic 1T' phases in MoS₂ 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 MoS₂ transistors as promising building blocks for efficient RC systems capable of processing complex temporal patterns, offering enhanced scalability, and practical applicability in neuromorphic computation.