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 H & eacute;non 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.