Reconfigurable Low-Power TiO2 Memristor for Integration of Artificial Synapse and Nociceptor

神经形态工程学 记忆电阻器 材料科学 神经科学 突触 纳米技术 计算机科学 人工智能 人工神经网络 电子工程 生物 工程类
作者
Mousam Charan Sahu,Anjan Kumar Jena,Sameer Kumar Mallik,Suman Roy,Sandhyarani Sahoo,Sandhyarani Sahoo,R. S. Ajimsha,Pankaj Misra,Satyaprakash Sahoo,Satyaprakash Sahoo
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:15 (21): 25713-25725 被引量:65
标识
DOI:10.1021/acsami.3c02727
摘要

Bio-mimetic advanced electronic systems are emerging rapidly, engrossing their applications in neuromorphic computing, humanoid robotics, tactile sensors, and so forth. The biological synaptic and nociceptive functions are governed by intricate neurotransmitter dynamics that involve both short-term and long-term plasticity. To emulate the neuronal dynamics in an electronic device, an Ag/TiO2/Pt/SiO2/Si memristor is fabricated, exhibiting compliance current controlled reversible transition of volatile switching (VS) and non-volatile switching (NVS). The origin of the VS and NVS depends on the diameter of the conducting filament, which is explained using a field-induced nucleation theory and validated by temporal current response measurements. The switching delay of the device is used to determine the characteristic nociceptive behaviors such as threshold, relaxation, inadaptation, allodynia, and hyperalgesia. The short-term and long-term retention loss attributed to the VS and NVS, respectively, is used to emulate short-term memory and long-term memory of the biological brain in a single device. More importantly, synergistically modulating the VS–NVS transition, the complex spike rate-dependent (SRDP) and spike time-dependent plasticity (STDP) with a weight change of up to 600% is demonstrated in the same device, which is the highest reported so far for TiO2 memristors. Furthermore, the device exhibits very low power consumption, ∼3.76 pJ/spike, and can imitate synaptic and nociceptive functions. The consolidation of complex nociceptive and synaptic behavior in a single memristor facilitates low-power integration of scalable intelligent sensors and neuromorphic devices.
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