Three Musketeers: demonstration of multilevel memory, selector, and synaptic behaviors from an Ag-GeTe based chalcogenide material
- Authors
- Yu, Min Ji; Son, Kyung Rock; Khot, Atul C.; Kang, Dae Yun; Sung, Ji Hoon; Jang, Il Gyu; Dange, Yogesh D.; Dongale, Tukaram D.; Kim, Tae Geun
- Issue Date
- 11월-2021
- Publisher
- ELSEVIER
- Keywords
- Amorphous Ag-GeTe; Convolutional neural network edge detection; Multilevel resistive switching; Neuromorphic computing; Selector device
- Citation
- JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T, v.15, pp.1984 - 1995
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
- Volume
- 15
- Start Page
- 1984
- End Page
- 1995
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/135974
- DOI
- 10.1016/j.jmrt.2021.09.044
- ISSN
- 2238-7854
- Abstract
- Functional neuronal computing systems that support information diversification require high-density memory with selector devices to reduce leakage current in cross-point architectures, which drives us to develop a functional switching layer that operates as three distinct devices, namely non-volatile memory, selector, and synaptic devices, using a GeTe-based single material system. In this study, amorphous Ag-GeTe switching layers are engineered by doping with Te species to achieve either resistive switching (RS) or threshold switching properties. The Ag/Ag-GeTe/Ag memory device exhibits multilevel characteris-tics via a tunable compliance current approach. By comparison, Ag/Ag-GeTex/Ag selector device provides excellent selectivity (>10(6)) with a very low OFF-current (similar to 10(-11) A). The RS mechanism for memory and selector devices is interrogated by using conductive atomic force microscopy. Moreover, the Ag/Ag-GeTe/Ag RS device mimics a cohort of basic and complex synaptic plasticity properties, including potentiation-depression and four-spike time-dependent plasticity rules that include asymmetric Hebbian, asymmetric anti-Hebbian, symmetric Hebbian, and symmetric anti-Hebbian learning rules. The capability of the synaptic devices to detect image edges is demonstrated by using a convolution neural network. The present work showcases the multi-functionality of Ag-GeTe materials, which will likely emerge as a prominent candidate for high-density cross-point architecture-based neuromorphic computing systems. (C) 2021 The Author(s). Published by Elsevier B.V.
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