Yuki Shibayama, D. Yamakawa, M. Kimura, Y. Nakashima
{"title":"in - ga - zn - o薄膜突触在神经网络中的应用","authors":"Yuki Shibayama, D. Yamakawa, M. Kimura, Y. Nakashima","doi":"10.1109/IMFEDK.2018.8581971","DOIUrl":null,"url":null,"abstract":"We fabricated In-Ga-Zn-O (IGZO) thin film synapses for neural networks using an LSI. The current flowing in the IGZO thin film degraded gradually. It shows a sufficient degradation in electrical characteristics, and it can be used for the modified Hebbian learning rule proposed by the authors.","PeriodicalId":434417,"journal":{"name":"2018 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"In-Ga-Zn-O Thin Film Synapse in Neural Network Using LSI\",\"authors\":\"Yuki Shibayama, D. Yamakawa, M. Kimura, Y. Nakashima\",\"doi\":\"10.1109/IMFEDK.2018.8581971\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We fabricated In-Ga-Zn-O (IGZO) thin film synapses for neural networks using an LSI. The current flowing in the IGZO thin film degraded gradually. It shows a sufficient degradation in electrical characteristics, and it can be used for the modified Hebbian learning rule proposed by the authors.\",\"PeriodicalId\":434417,\"journal\":{\"name\":\"2018 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMFEDK.2018.8581971\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMFEDK.2018.8581971","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In-Ga-Zn-O Thin Film Synapse in Neural Network Using LSI
We fabricated In-Ga-Zn-O (IGZO) thin film synapses for neural networks using an LSI. The current flowing in the IGZO thin film degraded gradually. It shows a sufficient degradation in electrical characteristics, and it can be used for the modified Hebbian learning rule proposed by the authors.