Fully hardware-oriented physical reservoir computing using 3D vertical resistive switching memory with different bottom electrodes

Abstract

Reservoir computing (RC) is a promising machine learning paradigm that processes input data using a fixed random network. However, implementing both reservoir and readout layers typically requires multiple devices and additional fabrication steps. To overcome this, we introduce a fully integrated RC system based on a vertically stacked Ta/Ta₂O₅/HfO₂/W and TiN vertical-resistive random-access memory (VRRAM) structure, which can select short-term and long-term memory in VRRAM structure with different bottom electrodes. The volatile VRRAM serves as a physical reservoir, utilizing its fading memory and nonlinearity to capture temporal dependencies, while the nonvolatile VRRAM functions as a readout network with multi-level storage capability and high linearity. Neuromorphic simulations show that using conductance variations as synaptic weights enables pattern recognition accuracy above 93.14%, successfully replicating biological synaptic behaviors. Finally, the proposed Cyclic RC structure effectively processes temporal patterns, achieving strong performance with an NRMSE of 0.2123 for waveform classification and 0.2377 for Hénon map prediction. These findings underscore the potential of hardware-efficient, short-term memory-based architectures for forecasting nonlinear dynamical systems and advancing neuromorphic computing applications.