Publication date: 15th December 2025
Different classes of oxide-based memristors are being actively investigated as enabling technologies for neuromorphic systems and unconventional computing paradigms. These devices can replicate synaptic and neuronal functionalities directly in hardware, operate as computing units for in-memory processing within neural networks, and serve as fundamental elements in nonlinear dynamical circuits. In this context, oxide-based memristors exhibiting both volatile and non-volatile resistive switching are extensively studied to implement diverse computing functionalities [1].
The first part of the presentation will focus on our latest results concerning volatile electrochemical memristors based on Ag/SiOₓ/Pt structure [2], and how their relaxation dynamics can be tailored by introducing an ultrathin Al₂O₃ layer (1–2 nm) via atomic layer deposition at the SiOₓ/Pt interface. We will examine the relationship between switching times and relaxation effects, which governs memristor dynamics and allows for multiple switching modes to emulate essential synaptic and neuronal functions. In the second part, I will introduce our recent advances on HfO₂-based analog memristors and their application in a memristor-driven circuit inspired by the Murali–Lakshmanan–Chua (MLC) architecture [3]. This circuit leverages the programmable and nonlinear properties of Pt/HfO₂/TiN memristor devices and enables single-node reservoir computing for various nonlinear classification tasks and real-time information processing [4].
Acknowledgements. This work has been partially funded by Ministero delle Imprese e del Made in Italy (MIMIT) under IPCEI Microelettronica 2, project MicroTech_for_Green; and by the COSMO project (Grant PRIN 2017LSCR4K002), funded by the Italian Ministry of Education, University and Research (MIUR).
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