Physical Reservoir Computing System via Hybrid Ferroelectric-Ionic Transistors

Abstract

In-materia computing, harnessing material complexity for energy-efficient computation, drives breakthroughs in constructing physical reservoir computing (PRC), a promising paradigm for energy-efficient handling of dynamic temporal tasks. However, the integration of PRC components into complementary metal-oxide-semiconductor (CMOS)-compatible and very large-scale integration (VLSI)-scalable platforms remains challenging, particularly in two-terminal devices that utilize exotic material systems. Herein, the integration of hafnia-based hybrid ferroelectric-ionic field-effect transistors (FETs) is reported for in-materia PRC with all-FET structures. Hybrid FETs with dual long-term polarization switching and short-term ionic switching functionality are integrated into PRC via wafer-scale atomic layer deposition on a single wafer, guaranteeing CMOS compatibility and VLSI scalability with the deposition technique and materials in modern microelectronics. The proposed PRC system effectively processes multimodal biosignals, including electroencephalogram, electrocardiogram, and electromyogram, demonstrating superior performance compared to conventional two-terminal device-based systems by enabling adaptive temporal dynamics and tunable memory characteristics. These results pave the way for hardware-implemented dynamic neural networks that are highly energy- and area-efficient, thus advancing practical edge AI applications in healthcare and real-time signal processing.