Efficient Resistive Switching and Spike Rate Dependent Plasticity in a New CuCrO2 Memristor for Plausible Neuromorphic Systems
| Autor: | Bharathwaj Suresh, Pranab Biswas, Daragh Mullarkey, Igor V. Shvets, Souvik Kundu, Ainur Zhussupbekova, Pavan Kumar Reddy Boppidi, P. Michael Preetam Raj |
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| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Spiking neural network Materials science business.industry Conductance Memristor Plasticity 01 natural sciences Electronic Optical and Magnetic Materials law.invention Hebbian theory Neuromorphic engineering Pulse-amplitude modulation law 0103 physical sciences Band diagram Optoelectronics Electrical and Electronic Engineering business |
| Zdroj: | IEEE Transactions on Electron Devices. 67:3451-3458 |
| ISSN: | 1557-9646 0018-9383 |
| DOI: | 10.1109/ted.2020.2999324 |
| Popis: | In this article, we introduce a new class of p-type transparent conductive oxide (TCO) CuCrO2 (150 nm) heterogeneously integrated onto fluorine doped tin oxide (FTO)/glass for forming-free memristor-based neuromorphic applications. The fabricated Al/CuCrO2/FTO memristors demonstrate a reliable bipolar resistive switching with an ON/ OFF ratio of 1000. The retention of the device was found to be steady even beyond 106 s, which demonstrates its nonvolatility. The current–voltage ( ${I}$ – ${V}$ ) characteristics were fit to evaluate its transport properties and a band diagram was projected to have a better insight of the device operational principles. To validate the experimental observations, a new model has been developed, and the simulated ${I}$ – ${V}$ behavior was analogous to the experimental one. Efforts were then devoted to observe long-term potentiation (LTP) and long-term depression (LTD) utilizing identical but opposite pulses to evaluate the device’s efficacy for synaptic applications. The synaptic behavior was well controlled by the pulse (pulse amplitude and width) variations. The conductance change was found to be symmetric and then saturated, which reflects the popular biological Hebbian rules. Finally, a long-term synaptic modulation has been implemented by establishing the spike rate dependent plasticity (SRDP) rule, which is a part of spiking neural networks and advantageous to mimic the brain’s capability at low power. All the obtained experimental results were systematically corroborated by neural network simulation. Overall, our approach provides a new road map toward the development of TCO-based alternative memristors, which can be employed to mimic the synaptic plasticity for energy-efficient bioinspired neuromorphic systems and non-von Neumann computer architectures. |
| Databáze: | OpenAIRE |
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