Predictive modeling of mixed abrasive slurry for enhanced performance in tungsten chemical mechanical polishing: A particle number concentration approach.
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| Title: | Predictive modeling of mixed abrasive slurry for enhanced performance in tungsten chemical mechanical polishing: A particle number concentration approach. |
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| Authors: | Back, Geumji1, Oh, Seungjun2, Lee, Dongho2, Kim, Taesung1,2, tkim@skku.edu |
| Source: | Materials Science in Semiconductor Processing; Feb2026, Vol. 202, pN.PAG-N.PAG, 1p |
| Database: | Applied Science & Technology Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 189183997 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=189183997 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.mssp.2025.110119 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Titles: – TitleFull: Predictive modeling of mixed abrasive slurry for enhanced performance in tungsten chemical mechanical polishing: A particle number concentration approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Back, Geumji – PersonEntity: Name: NameFull: Oh, Seungjun – PersonEntity: Name: NameFull: Lee, Dongho – PersonEntity: Name: NameFull: Kim, Taesung IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13698001 Numbering: – Type: volume Value: 202 Titles: – TitleFull: Materials Science in Semiconductor Processing Type: main |
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