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 (AUTHOR), Oh, Seungjun2 (AUTHOR), Lee, Dongho2 (AUTHOR), Kim, Taesung1,2 (AUTHOR) tkim@skku.edu |
| Source: | Materials Science in Semiconductor Processing. Feb2026, Vol. 202, pN.PAG-N.PAG. 1p. |
| Subjects: | Prediction models, Tungsten, Particle size distribution, Chemical properties, Size reduction of materials |
| Abstract: | As semiconductor devices continue to scale down and incorporate increasingly complex multilayer structures, chemical mechanical polishing (CMP) faces critical challenges in simultaneously achieving high removal rates (RR) and excellent within-wafer non-uniformity (WIWNU). Conventional single abrasive slurries and mass concentration–based mixed abrasive slurry (MAS) models have reached their performance limits, primarily because they disregard the actual number distribution of particles and the effects of polydispersity. In addition, traditional light-scattering analysis for particle size measurement cannot accurately determine the mixing ratios of different sized abrasives in polydisperse conditions. To overcome these limitations, this study used a scanning mobility particle sizer (SMPS) to precisely quantify particle number concentrations in mixed slurries and incorporated these data into a predictive MAS model. The contact area analysis, further supported by packing density calculations, indicated that the maximum contact area occurs at an optimal composition of 55 % large particles (113 nm) and 45 % small particles (55 nm). Tungsten CMP experiments verified these predictions, achieving up to a 6.45-fold increase in removal rate and a 54.1 % reduction in WIWNU compared with single abrasive slurries under same total particle counts. Moreover, the removal rate exhibited a strong linear correlation with the calculated total contact area (R2 = 0.90), while the number-based model substantially reduced prediction errors relative to conventional mass-based models (RMSE: 129.84 vs. 223.38; MAPE: 17.55 % vs. 24.12 %). These results demonstrate that particle number concentration-based modeling provides a quantitative basis for slurry optimization, enabling the simultaneous enhancement of efficiency and uniformity in advanced tungsten CMP. • A predictive model for mixed abrasive slurries (MAS) was developed to accurately calculate total contact area. • Model validation identified an optimal 55 % large particle to 45 % small particle mixing ratio, yielding maximal contact area. • CMP results demonstrated higher removal rate and improvement in uniformity, with a linear correlation (R2 = 0.90). [ABSTRACT FROM AUTHOR] |
| Copyright of Materials Science in Semiconductor Processing is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 189183997 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predictive modeling of mixed abrasive slurry for enhanced performance in tungsten chemical mechanical polishing: A particle number concentration approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Back%2C+Geumji%22">Back, Geumji</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Oh%2C+Seungjun%22">Oh, Seungjun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Dongho%22">Lee, Dongho</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Taesung%22">Kim, Taesung</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> tkim@skku.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Materials+Science+in+Semiconductor+Processing%22">Materials Science in Semiconductor Processing</searchLink>. Feb2026, Vol. 202, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Tungsten%22">Tungsten</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+size+distribution%22">Particle size distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+properties%22">Chemical properties</searchLink><br /><searchLink fieldCode="DE" term="%22Size+reduction+of+materials%22">Size reduction of materials</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As semiconductor devices continue to scale down and incorporate increasingly complex multilayer structures, chemical mechanical polishing (CMP) faces critical challenges in simultaneously achieving high removal rates (RR) and excellent within-wafer non-uniformity (WIWNU). Conventional single abrasive slurries and mass concentration–based mixed abrasive slurry (MAS) models have reached their performance limits, primarily because they disregard the actual number distribution of particles and the effects of polydispersity. In addition, traditional light-scattering analysis for particle size measurement cannot accurately determine the mixing ratios of different sized abrasives in polydisperse conditions. To overcome these limitations, this study used a scanning mobility particle sizer (SMPS) to precisely quantify particle number concentrations in mixed slurries and incorporated these data into a predictive MAS model. The contact area analysis, further supported by packing density calculations, indicated that the maximum contact area occurs at an optimal composition of 55 % large particles (113 nm) and 45 % small particles (55 nm). Tungsten CMP experiments verified these predictions, achieving up to a 6.45-fold increase in removal rate and a 54.1 % reduction in WIWNU compared with single abrasive slurries under same total particle counts. Moreover, the removal rate exhibited a strong linear correlation with the calculated total contact area (R2 = 0.90), while the number-based model substantially reduced prediction errors relative to conventional mass-based models (RMSE: 129.84 vs. 223.38; MAPE: 17.55 % vs. 24.12 %). These results demonstrate that particle number concentration-based modeling provides a quantitative basis for slurry optimization, enabling the simultaneous enhancement of efficiency and uniformity in advanced tungsten CMP. • A predictive model for mixed abrasive slurries (MAS) was developed to accurately calculate total contact area. • Model validation identified an optimal 55 % large particle to 45 % small particle mixing ratio, yielding maximal contact area. • CMP results demonstrated higher removal rate and improvement in uniformity, with a linear correlation (R2 = 0.90). [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Materials Science in Semiconductor Processing is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| 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 Subjects: – SubjectFull: Prediction models Type: general – SubjectFull: Tungsten Type: general – SubjectFull: Particle size distribution Type: general – SubjectFull: Chemical properties Type: general – SubjectFull: Size reduction of materials Type: general 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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