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Tyrosine-mediated analog resistive switching for artificial neural networks

Nano Research 2023年第1期16卷 858-864页

作者： Min-Kyu Song Seok Daniel Namgung Hojung Lee Jeong Hyun Yoon Young-Woong Song Kang Hee Cho Yoon-Sik Lee Jong-Seok Lee Ki Tae Nam Jang-Yeon Kwon School of Integrated Technology Yonsei UniversityIncheon 21983Republic of Korea Department of Mechanical Engineering Massachusetts Institute of TechnologyMA 02139USA Research Laboratory of Electronics Massachusetts Institute of TechnologyMA 02139USA Department of materials Science and Engineering Seoul National UniversitySeoul 08826Republic of Korea Soft Foundry Seoul National UniversitySeoul 08826Republic of Korea School of Chemical and Biological Engineering Seoul National UniversitySeoul 08826Republic of Korea

The fourth industrial revolution indispensably brings explosive data processing and storage;thus,a new computing paradigm based on artificial intelligence-enabling device structure is urgently *** have received considerable attention in this regard because of their ability to process and store data at the same ***,fundamental problems with abrupt switching characteristics limit their practical *** address this problem,we utilized the concept of metaplasticity inspired by biosystems and observed gradual switching in the peptide-based memristor at high proton *** unexpectedly high slope value>1.7 in the logI–V curve at low voltage(≤400 mV)was considered the main origin,and it might arise from the modulatory response of proton ions on the threshold of Ag ion migration in the peptide *** the obtained gradual switching property at high proton conductivity,the device showed significantly increased accuracy of image recognition(~82.5%).We believe that such a demonstration not only contributes to the practical application of neuromorphic devices but also expands the bioinspired functional synthetic platform.

关键词： artificial synapse neuromorphic computing proton conductor peptide materials artificial neural network

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