Ferroelectric Devices as Physical Reservoirs: Enabling Nonlinear Dynamics and Memory in Neuromorphic Systems

Abstract

Reservoir computing (RC) provides a training efficient alternative to recurrent neural networks by fixing recurrent weights and training only a linear readout. In hardware, physical reservoirs harness intrinsic device dynamics to supply the three requisites for temporal computation: nonlinearity, short term memory, and resulting high dimensional state richness. This review summarises RC fundamentals and maps device requirements onto materials properties including domain nucleation, hysteresis, depolarisation driven volatility, and multiscale relaxation. We survey ferroelectric platforms—hafnia based ferroelectric field effect transistors (FeFETs), ferroelectric tunnel junctions (FTJs), and ferroelectric thin film transistors (FeTFTs), and their antiferroelectric variants—that natively deliver nonlinear input–state mapping, tunable fading memory, and rich intermediate states. Implementation strategies include multiplexing and single device reservoirs, evaluated against metrics for memory capacity and energy–latency–accuracy. Emphasis is placed on complementary-metal-oxide-semiconductor compatible HfO2 for scalability, fast switching, and low voltage operation. Reliability and variability are reframed as resources through interface and defect engineering. Ferroelectrics emerge as energy efficient reservoirs for robust temporal inference at the edge.