Volatile and Non‐Volatile Ferroelectric HZO for Low‐Power Reservoir Computing
Yichun Xu, Davide Rossetti, Shiva Asapu, Taehwan Moon, Jian Zhao, Ruoyu Zhao, Seung Ju Kim, Tong Wang, Michele Bonnin, Alon Ascoli, Andreu L. Glasmann, Sina Najmaei, Fernando Corinto, R. Stanley Williams, J. Joshua YangABSTRACT
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 Hf 0 . 8 Zr 0 . 2 O 2 provides stable ferroelectric (FE) behavior for non‐volatile weight storage, whereas Hf 0 . 2 Zr 0 . 8 O 2 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.