Bipolar Analog Memristors as Artificial Synapses for Neuromorphic Computing

Synaptic devices with bipolar analog resistive switching behavior are the building blocks for memristor-based neuromorphic computing. In this work, a fully complementary metal-oxide semiconductor (CMOS)-compatible, forming-free, and non-filamentary memristive device (Pd/Al<sub>2</sub>O&l...

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Bibliographic Details
Main Authors: Rui Wang, Tuo Shi, Xumeng Zhang, Wei Wang, Jinsong Wei, Jian Lu, Xiaolong Zhao, Zuheng Wu, Rongrong Cao, Shibing Long, Qi Liu, Ming Liu
Format: Article
Language:English
Published: MDPI AG 2018-10-01
Series:Materials
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Online Access:https://www.mdpi.com/1996-1944/11/11/2102
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Summary:Synaptic devices with bipolar analog resistive switching behavior are the building blocks for memristor-based neuromorphic computing. In this work, a fully complementary metal-oxide semiconductor (CMOS)-compatible, forming-free, and non-filamentary memristive device (Pd/Al<sub>2</sub>O<sub>3</sub>/TaO<sub>x</sub>/Ta) with bipolar analog switching behavior is reported as an artificial synapse for neuromorphic computing. Synaptic functions, including long-term potentiation/depression, paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are implemented based on this device; the switching energy is around 50 pJ per spike. Furthermore, for applications in artificial neural networks (ANN), determined target conductance states with little deviation (&lt;1%) can be obtained with random initial states. However, the device shows non-linear conductance change characteristics, and a nearly linear conductance change behavior is obtained by optimizing the training scheme. Based on these results, the device is a promising emulator for biology synapses, which could be of great benefit to memristor-based neuromorphic computing.
ISSN:1996-1944