Analog-Digital Hybridity of Resistive Switching in Ion-Irradiated BiFeO3 Memristor for Synergistic Neuromorphic Functionality and Artificial Learning

Abstract

Memristors-based neuromorphic devices represent emerging computing architectures to perform complex tasks by outpacing the traditional Von-Neumann architectures in terms of speed, and energy efficiency. In this work, the resistive switching (RS) behavior of sol-gel grown and ion-irradiated BFO films is investigated under electrical stimulus. The Ag/BFO/FTO memristors emulate a combination of digital and analog RS behavior within a single device. The possible mechanism of analog digital hybridity is addressed by considering the formation of the conducting filament by oxygen vacancies, Ag⁺ ions and Schottky barrier height modulation. The ion-irradiated BFO samples are analyzed using the Raman, XRD, and XPS studies. To uphold bioinspired synaptic actions, crucial synaptic functionalities like pair-pulse facilitation and long-term potentiation/depression are effectively achieved. More intricate synaptic behaviors are also demonstrated such as spike-time-dependent plasticity and Pavlovian classical conditioning, which represent the prominent attributes of both learning and forgetting behavior. Additionally, high pattern recognition accuracy (96.1%) is achieved in an artificial neural network simulation by using the synaptic weights of the memristors. This synergistic effect of digital and analog RS in ion-irradiated BFO can be beneficial for the emulation of complex learning behavior as well as its incorporation into low-power neuromorphic computing.