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溅射沉积非晶态 SrVO 基忆阻器在神经形态计算中的应用。

Sputtering-deposited amorphous SrVO-based memristor for use in neuromorphic computing.

机构信息

Department of Nanophotonics, Korea University, Seoul, 02841, Korea.

Department of Materials Science and Engineering, Korea University, Seoul, 02841, Korea.

出版信息

Sci Rep. 2020 Apr 1;10(1):5761. doi: 10.1038/s41598-020-62642-3.

DOI:10.1038/s41598-020-62642-3
PMID:32238846
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7113278/
Abstract

The development of brain-inspired neuromorphic computing, including artificial intelligence (AI) and machine learning, is of considerable importance because of the rapid growth in hardware and software capacities, which allows for the efficient handling of big data. Devices for neuromorphic computing must satisfy basic requirements such as multilevel states, high operating speeds, low energy consumption, and sufficient endurance, retention and linearity. In this study, inorganic perovskite-type amorphous strontium vanadate (a-SrVO: a-SVO) synthesized at room temperature is utilized to produce a high-performance memristor that demonstrates nonvolatile multilevel resistive switching and synaptic characteristics. Analysis of the electrical characteristics indicates that the a-SVO memristor illustrates typical bipolar resistive switching behavior. Multilevel resistance states are also observed in the off-to-on and on-to-off transition processes. The retention resistance of the a-SVO memristor is shown to not significantly change for a period of 2 × 10 s. The conduction mechanism operating within the Ag/a-SVO/Pt memristor is ascribed to the formation of Ag-based filaments. Nonlinear neural network simulations are also conducted to evaluate the synaptic behavior. These results demonstrate that a-SVO-based memristors hold great promise for use in high-performance neuromorphic computing devices.

摘要

脑启发的神经形态计算的发展,包括人工智能(AI)和机器学习,由于硬件和软件容量的快速增长,具有相当重要的意义,这使得能够有效地处理大数据。神经形态计算设备必须满足多级状态、高速运行、低能耗和足够的耐久性、保持力和线性度等基本要求。在本研究中,利用在室温下合成的无机钙钛矿型非晶氧化钒(a-SrVO: a-SVO) 来制造高性能忆阻器,该忆阻器表现出非易失性多级电阻开关和突触特性。电特性分析表明,a-SVO 忆阻器表现出典型的双极电阻开关行为。在关断到导通和导通到关断的转换过程中也观察到了多级电阻状态。a-SVO 忆阻器的保持电阻在 2×10 s 的时间内没有明显变化。Ag/a-SVO/Pt 忆阻器中的导电机理归因于 Ag 基丝的形成。还进行了非线性神经网络模拟来评估突触行为。这些结果表明,基于 a-SVO 的忆阻器有望用于高性能神经形态计算设备。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/31ab680bbac9/41598_2020_62642_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/bec7529d151d/41598_2020_62642_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/7914fefd3c53/41598_2020_62642_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/e38a6cb274d4/41598_2020_62642_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/73df5ab2b8c0/41598_2020_62642_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/3eb5dbbf2bcd/41598_2020_62642_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/31ab680bbac9/41598_2020_62642_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/bec7529d151d/41598_2020_62642_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/7914fefd3c53/41598_2020_62642_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/e38a6cb274d4/41598_2020_62642_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/73df5ab2b8c0/41598_2020_62642_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/3eb5dbbf2bcd/41598_2020_62642_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c882/7113278/31ab680bbac9/41598_2020_62642_Fig6_HTML.jpg

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