Volatile and Non‐Volatile Ferroelectric HZO for Low‐Power Reservoir Computing

We present a capacitive reservoir computing system based on hafnium-zirconium oxide (HZO) capacitors that integrates intrinsically volatile and non-volatile functionalities within a single material platform. Through compositional control, fabricated Hf0 .8 Zr0 .2 O2 provides stable ferroelectric (FE) behavior for non-volatile weight storage, whereas Hf0 .2 Zr0 .8 O2 exhibits volatile antiferroelectric-like (VFE) dynamics characterized by fading memory and nonlinear responses. Using polarization-voltage, capacitance-voltage, and dynamic transient measurements from these devices, we formulate and calibrate a modified Landau-Khalatnikov compact model that accurately reproduces both FE and VFE switching behaviors. Implemented and validated in Cadence Spectre, the model enables realistic large-scale circuit simulations. Leveraging this framework, a hybrid reservoir network combining an FE HZO capacitor array with transistor-coupled VFE HZO nodes was developed. Co-simulation between Python-based system evaluation and Cadence circuit modeling achieves 93.5% accuracy on the Voice-MNIST classification task with ultralow energy consumption ( ∼ 184 fJ per cell and ∼ 1.07 pJ per node per inference). These results establish a unified HZO-based device-circuit-system framework for scalable, CMOS-compatible, and energy-efficient temporal neuromorphic computing.