DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, S. | - |
dc.contributor.author | Hwang, H. | - |
dc.contributor.author | Yoo, I.K. | - |
dc.date.accessioned | 2019-12-03T12:51:12Z | - |
dc.date.available | 2019-12-03T12:51:12Z | - |
dc.date.created | 2019-10-10 | - |
dc.date.issued | 2019-09 | - |
dc.identifier.issn | 2166-532X | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/100226 | - |
dc.description.abstract | Ferroelectric materials are promising candidates for synaptic weight elements in neural network hardware because of their nonvolatile multilevel memory effect. This feature is crucial for their use in mobile applications such as inference when vector matrix multiplication is performed during portable artificial intelligence service. In addition, the adaptive learning effect in ferroelectric polarization has gained considerable research attention for reducing the CMOS circuit overhead of an integrator and amplifier with an activation function. In spite of their potential for a weight and a neuron, material issues have been pointed out for commercialization in conjunction with CMOS processing and device structures. Herein, we review ferroelectric synaptic weights and neurons from the viewpoint of materials in relation to device operation, along with discussions and suggestions for improvement. Moreover, we discuss the reliability of HfO2 as an emerging material and suggest methods to overcome the scaling issue of ferroelectrics. | - |
dc.language | English | - |
dc.publisher | AMER INST PHYSICS | - |
dc.relation.isPartOf | APL MATERIALS | - |
dc.title | Ferroelectric materials for neuromorphic computing | - |
dc.type | Article | - |
dc.identifier.doi | 10.1063/1.5108562 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | APL MATERIALS, v.7, no.9 | - |
dc.identifier.wosid | 000489245900002 | - |
dc.citation.number | 9 | - |
dc.citation.title | APL MATERIALS | - |
dc.citation.volume | 7 | - |
dc.contributor.affiliatedAuthor | Oh, S. | - |
dc.contributor.affiliatedAuthor | Hwang, H. | - |
dc.contributor.affiliatedAuthor | Yoo, I.K. | - |
dc.identifier.scopusid | 2-s2.0-85072620513 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | Activation functions | - |
dc.subject.keywordPlus | Emerging materials | - |
dc.subject.keywordPlus | Ferroelectric polarization | - |
dc.subject.keywordPlus | Mobile applications | - |
dc.subject.keywordPlus | Multilevel memory | - |
dc.subject.keywordPlus | Neural network hardware | - |
dc.subject.keywordPlus | Neuromorphic computing | - |
dc.subject.keywordPlus | Vector-matrix multiplications | - |
dc.subject.keywordPlus | Ferroelectric materials | - |
dc.subject.keywordPlus | CMOS integrated circuits | - |
dc.subject.keywordPlus | Ferroelectricity | - |
dc.subject.keywordPlus | Hafnium oxides | - |
dc.subject.keywordPlus | Neural networks | - |
dc.relation.journalWebOfScienceCategory | Nanoscience & Nanotechnology | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
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