Switching Dynamics in Anti-Ferroelectric Transistor for Multimodal Reservoir Computing

Abstract

Spatial-temporal time series analysis and forecasting are crucial for understanding dynamic systems and making informed decisions. Recurrent neural networks (RNNs) have paved the way for reservoir computing (RC), a method enabling effective temporal information processing at low training costs. While software-based RC performs well, physical RC systems face challenges like slow processing speed and limited state richness, leading to high hardware costs. This study introduces an innovative approach, i.e., the antiferroelectric field effect transistor-based RC (AFeFET-based RC) system for efficient temporal data processing. By exploiting the fading memory property inherent in hafnium oxide-based antiferroelectric material, this system demonstrates promise for physical RC implementation. Moreover, it leverages the light sensitivity of 2D molybdenum disulfide (MoS2) channels for controllable temporal dynamics under electrical and optical stimuli. This dual-mode modulation significantly enriches the reservoir state, boosting overall system performance. Experimental tests on standard benchmarking tasks using the AFeFET-based RC system yielded impressive accuracy results (95.4%) in spoken-digit recognition and a remarkable normalized root mean square error (NRMSE) of 0.015 in Mackey–Glass time series prediction.