神经形态工程学
电阻随机存取存储器
材料科学
光电子学
电阻式触摸屏
纳米技术
复合数
计算机科学
复合材料
电气工程
人工神经网络
电压
人工智能
工程类
作者
Bisweswar Santra,Gangadhar Das,Giuliana Aquilanti,A. Kanjilal
摘要
The advancement of neuromorphic computing in resistive random-access memory (RRAM) is crucial for the rapid expansion of artificial intelligence. Conventional metal oxide-based RRAM faces challenges in mimicking synaptic activity, leading to the exploration of new resistive switching (RS) materials. This study introduces a ZnO@β-SiC composite-based RRAM device that exhibits biological synapse-like functionality. The device shows self-compliance and forming-free RS at ∼0.8 V, where it also mimics synaptic responses such as potentiation, depression, and paired-pulse facilitation at low voltage stimuli (∼0.6 V, 40 ms) with learning and forgetting behavior. Moreover, the synaptic plasticity is analyzed through spike rate dependent plasticity, spike number dependent plasticity, and spike time dependent plasticity. Further, the transition from short-term plasticity to long-term plasticity is observed under more training pulses and lower interval stimuli. The observed RS mechanism and synaptic functionalities are explained by the electric field-driven formation and dissolution of conducting filaments of oxygen vacancies. The chemical properties and local electronic structure have been examined by x-ray photoelectron spectroscopy and x-ray absorption spectroscopy. To elucidate the atomistic memristive behavior and the contribution of different electrical parameters in RRAM, detailed conductive atomic-force microscopy and impedance analysis have been carried out.
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