Versatile memristor for memory and neuromorphic computing. Issue 3 (22nd January 2022)

• Record Type: Journal Article
• Authors: Guo, Tao; Pan, Kangqiang; Jiao, Yixuan; Sun, Bai; Du, Cheng; Mills, Joel P.; Chen, Zuolong; Zhao, Xiaoye; Wei, Lan; Zhou, Y. Norman; Wu, Yimin A.
• Is Part Of: Nanoscale horizons. Volume 7:Issue 3(2022)
• Page Start: 299
• Page End: 310
• Publication Date: 2022-01-22
• Subjects: Nanoscience -- Periodicals; Nanotechnology -- Periodicals; 620.505
• DOI: 10.1039/d1nh00481f
• Languages: English
• ISSNs: 2055-6756

Abstract

Abstract : The memristor is a promising candidate to implement high-density memory and neuromorphic computing. Abstract : The memristor is a promising candidate to implement high-density memory and neuromorphic computing. Based on the characteristic retention time, memristors are classified into volatile and non-volatile types. However, a single memristor generally provides a specific function based on electronic performances, which poses roadblocks for further developing novel circuits. Versatile memristors exhibiting both volatile and non-volatile properties can provide multiple functions covering non-volatile memory and neuromorphic computing. In this work, a versatile memristor with volatile/non-volatile bifunctional properties was developed. Non-volatile functionality with a storage window of 4.0 × 10 5 was obtained. Meanwhile, the device can provide threshold volatile functionalities with a storage window of 7.0 × 10 4 and a rectification ratio of 4.0 × 10 4 . The leaky integrate-and-fire (LIF) neuron model and artificial synapse based on the device have been studied. Such a versatile memristor enables non-volatile memory, selectors, artificial neurons, and artificial synapses, which will provide advantages regarding circuit simplification, fabrication processes, and manufacturing costs.