Bipolar Analog Memristors as Artificial Synapses for Neuromorphic Computing

• Published in: Materials Vol. 11; no. 11; p. 2102
• Main Authors: Wang, Rui, Shi, Tuo, Zhang, Xumeng, Wang, Wei, Wei, Jinsong, Lu, Jian, Zhao, Xiaolong, Wu, Zuheng, Cao, Rongrong, Long, Shibing, Liu, Qi, Liu, Ming
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 26.10.2018; MDPI AG
• Subjects: artificial synapse; memristor; neuromorphic computing; artificial synapse; memristor; neuromorphic computing
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma11112102

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/Al₂O₃/TaO /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 (<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.