Text mining method to identify artificial intelligence technologies for the semiconductor industry in Korea
DC Field | Value | Language |
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dc.contributor.author | Cho, Insu | - |
dc.contributor.author | Ju, Yonghan | - |
dc.date.accessioned | 2024-04-08T13:30:19Z | - |
dc.date.available | 2024-04-08T13:30:19Z | - |
dc.date.issued | 2023-09 | - |
dc.identifier.issn | 0172-2190 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/49448 | - |
dc.description.abstract | Semiconductors are among the most important core technologies contributing to the Fourth Industrial Revolution. The United States, Taiwan, and China have been investing heavily in semiconductor research and development. To achieve international competitiveness in the semiconductor industry, Korea needs to establish a research and development (R&D) roadmap for small- and medium-sized enterprises (SMEs). Our study identified trends in the semiconductor industry by analyzing the characteristics of core technologies based on patents that disclose technologies instead of holding exclusive ownership. Specifically, we analyzed registered patents concerned with artificial intelligence and machine learning pertaining to the semiconductor industry, which are attracting considerable attention. Using the Korea Intellectual Property Rights Information Service database, we identified 3569 patent specifications related to AI technology and the semiconductor industry. The text mining and network analysis results indicated that the application of deep neural networks is the most important and affects various aspects of R&D. Particularly, AI technology is actively studied for monitoring manufacturing and etch processes. Additionally, technology convergence among virtual reality, visualization, smart factories, and etching technology was identified. The analysis results identify promising technologies related to semiconductors and provide insights that would enable SMEs in the Korean semiconductor industry to establish a technology roadmap. © 2023 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Elsevier Ltd | - |
dc.title | Text mining method to identify artificial intelligence technologies for the semiconductor industry in Korea | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.wpi.2023.102212 | - |
dc.identifier.bibliographicCitation | World Patent Information, v.74 | - |
dc.identifier.scopusid | 2-s2.0-85163437371 | - |
dc.citation.title | World Patent Information | - |
dc.citation.volume | 74 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S017221902300042X?via%3Dihub | - |
dc.publisher.location | 네델란드 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | International patent classification | - |
dc.subject.keywordAuthor | Network analysis | - |
dc.subject.keywordAuthor | Patent | - |
dc.subject.keywordAuthor | Semiconductor | - |
dc.subject.keywordAuthor | Text mining | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | esci | - |
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