{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:38:08Z","timestamp":1759883888084,"version":"build-2065373602"},"reference-count":37,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T00:00:00Z","timestamp":1587686400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","award":["20003655","20003789"],"award-info":[{"award-number":["20003655","20003789"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:sec><jats:label\/><jats:p>Memristive neural networks perform vector matrix multiplication efficiently, which is used for the accelerator of neuromorphic computing. To train the memristor cells in a memristive neural network, the analog conductance state of the memristor should be programmed in parallel; otherwise, the resulting long training time can limit the size of the neural network. Herein, a novel parallel programming method using the self\u2010limited analog switching behavior of the memristor is proposed. A Pt\/Ti:NbO<jats:sub><jats:italic>x<\/jats:italic><\/jats:sub>\/NbO<jats:sub><jats:italic>x<\/jats:italic><\/jats:sub>\/TiN charge trap memristor device for the programming demonstration is utilized, and a convolutional neural network is emulated to train the MNIST dataset, based on the device characteristics. In the simulation, the proposed programming method is able to reduce programming time to as low as 1\/130, compared with the sequential programming method. The simulation suggests that the programming time required by the proposed method is not affected by array size, which makes it very promising in a high\u2010density neural network.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202000014","type":"journal-article","created":{"date-parts":[[2020,4,3]],"date-time":"2020-04-03T11:32:24Z","timestamp":1585913544000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Parallel Operation of Self\u2010Limited Analog Programming for Fast Array\u2010Level Weight Programming and Update"],"prefix":"10.1002","volume":"2","author":[{"given":"Hanchan","family":"Song","sequence":"first","affiliation":[{"name":"Department of Materials Science and Engineering KAIST  291 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jangho","family":"An","sequence":"additional","affiliation":[{"name":"Department of Materials Science and 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