{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1360021393542315264.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1002/inf2.12315"}},{"identifier":{"@type":"URI","@value":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/inf2.12315"}},{"identifier":{"@type":"URI","@value":"https://onlinelibrary.wiley.com/doi/full-xml/10.1002/inf2.12315"}}],"dc:title":[{"@value":"Deep machine learning unravels the structural origin of mid‐gap states in chalcogenide glass for high‐density memory integration"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:title>Abstract</jats:title><jats:p>The recent development of three‐dimensional semiconductor integration technology demands a key component—the ovonic threshold switching (OTS) selector to suppress the current leakage in the high‐density memory chips. Yet, the unsatisfactory performance of existing OTS materials becomes the bottleneck of the industrial advancement. The sluggish development of OTS materials, which are usually made from chalcogenide glass, should be largely attributed to the insufficient understanding of the electronic structure in these materials, despite of intensive research in the past decade. Due to the heavy first‐principles computation on disordered systems, a universal theory to explain the origin of mid‐gap states (MGS), which are the key feature leading to the OTS behavior, is still lacking. To avoid the formidable computational tasks, we adopt machine learning method to understand and predict MGS in typical OTS materials. We build hundreds of chalcogenide glass models and collect major structural features from both short‐range order (SRO) and medium‐range order (MRO) of the amorphous cells. After training the artificial neural network using these features, the accuracy has reached ~95% when it recognizes MGS in new glass. By analyzing the synaptic weights of the input structural features, we discover that the bonding and coordination environments from SRO and particularly MRO are closely related to MGS. The trained model could be used in many other OTS chalcogenides after minor modification. The intelligent machine learning allows us to understand the OTS mechanism from vast amount of structural data without heavy computational tasks, providing a new strategy to design functional amorphous materials from first principles.<jats:boxed-text content-type=\"graphic\" position=\"anchor\"><jats:graphic xmlns:xlink=\"http://www.w3.org/1999/xlink\" mimetype=\"image/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic/inf212315-toc-0001-m.png\"><jats:alt-text>image</jats:alt-text></jats:graphic></jats:boxed-text></jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380021393542315266","@type":"Researcher","foaf:name":[{"@value":"Meng Xu"}],"jpcoar:affiliationName":[{"@value":"Wuhan National Laboratory for Optoelectronics School of Integrated Circuits & School of Optical and Electronic Information, Huazhong University of Science and Technology  Wuhan China"}]},{"@id":"https://cir.nii.ac.jp/crid/1380021393542315264","@type":"Researcher","foaf:name":[{"@value":"Ming Xu"}],"jpcoar:affiliationName":[{"@value":"Wuhan National Laboratory for Optoelectronics School of Integrated Circuits & School of Optical and Electronic Information, Huazhong University of Science and Technology  Wuhan China"},{"@value":"Hubei Yangtze Memory Laboratories  Wuhan China"}]},{"@id":"https://cir.nii.ac.jp/crid/1380021393542315265","@type":"Researcher","foaf:name":[{"@value":"Xiangshui Miao"}],"jpcoar:affiliationName":[{"@value":"Wuhan National Laboratory for Optoelectronics School of Integrated Circuits & School of Optical and Electronic Information, Huazhong University of Science and Technology  Wuhan China"},{"@value":"Hubei Yangtze Memory Laboratories  Wuhan China"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"25673165"},{"@type":"EISSN","@value":"25673165"}],"prism:publicationName":[{"@value":"InfoMat"}],"dc:publisher":[{"@value":"Wiley"}],"prism:publicationDate":"2022-04-13","prism:volume":"4","prism:number":"6","prism:startingPage":"12315"},"reviewed":"false","dc:rights":["http://creativecommons.org/licenses/by/4.0/"],"url":[{"@id":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/inf2.12315"},{"@id":"https://onlinelibrary.wiley.com/doi/full-xml/10.1002/inf2.12315"}],"createdAt":"2022-04-14","modifiedAt":"2023-08-24","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360865815506307456","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Origins of midgap states in Te-based Ovonic threshold switch materials"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1002/inf2.12315"},{"@type":"CROSSREF","@value":"10.1016/j.actamat.2023.119209_references_DOI_PQ5qxqm8gZBkBUeENY44ROUBsUF"}]}