Publication date: 15th April 2025
Title: In memory neuromorphic computing in ZnO@ß-SiC based artificial synaptic device
Abstract: The evolution of neuromorphic computing is crucial on the development of advanced resistive random-access memory (RRAM) technologies capable of emulating biological synapses, a key requirement for next-generation artificial intelligence systems. Traditional metal oxide-based RRAM devices, however, face limitations in accurately replicating synaptic behaviors. In this study, a ZnO@β-SiC composite-based RRAM device is introduced, demonstrating self compliant, forming-free resistive switching (RS) at approximately 0.8 V. Notably, the device emulates crucial synaptic functions such as potentiation, depression, and paired-pulse facilitation at low voltage stimuli (~0.6 V, 40 ms), exhibiting both learning and forgetting dynamics. Synaptic plasticity is further explored through spike-rate, spike-number, and spike timing dependent protocols. A transition from short-term to long-term plasticity is observed with increased training pulses and reduced interval durations. The underlying RS mechanism is attributed to electric field-induced formation and rupture of conductive filaments formed by oxygen vacancies. The material's chemical and electronic structures are analyzed using x-ray photoelectron and x-ray absorption spectroscopy, while conductive atomic force microscopy and impedance spectroscopy provide insights into its memristive behavior and electrical characteristics.
Prof. Aloke Kanjilal, shiv Nadar university
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