Artificial neural network-driven diffraction imaging for nanoscale optical critical dimension metrology in semiconductor microstructures.
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| Title: | Artificial neural network-driven diffraction imaging for nanoscale optical critical dimension metrology in semiconductor microstructures. |
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| Authors: | Yang, Fu-Sheng1 (AUTHOR), Chen, Liang-Chia1 (AUTHOR) lchen@ntu.edu.tw, Ho, Chao-Ching2 (AUTHOR) |
| Source: | International Journal of Advanced Manufacturing Technology. Feb2026, Vol. 142 Issue 7/8, p3499-3525. 27p. |
| Subjects: | Artificial neural networks, Optical diffraction, Semiconductor manufacturing, Metrology, Microelectronic packaging |
| Abstract: | This study presents an optical critical dimension (CD) measurement technique that integrates artificial neural networks (ANNs) with coherent optical scatterometry (COS) and back focal plane (BFP) imaging for three-dimensional (3D) advanced semiconductor packaging. The proposed method enables simultaneous measurement of line width, line spacing, and height in redistribution layer (RDL) structures by analyzing diffraction signatures from periodic structures. Compared with atomic force microscopy (AFM) and scanning electron microscopy (SEM), which typically require several minutes per measurement, the proposed approach achieves measurement times of less than 3 ms, corresponding to a two-order-of-magnitude improvement in throughput. In comparison to white-light interferometry and confocal microscopy, the method offers approximately a fivefold improvement in lateral resolution, accompanied by significantly higher measurement speed. Experimental validation against AFM demonstrates measurement biases below 2% for line width and spacing and below 1.2% for height. The simple optical configuration also results in lower implementation costs. These characteristics make the proposed method suitable for inline process control in RDL fabrication and 3D IC manufacturing. Current limitations include a restricted depth measurement range and reliance on finite-difference time-domain (FDTD) simulations, which will be addressed in future work. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature 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: 191452010 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial neural network-driven diffraction imaging for nanoscale optical critical dimension metrology in semiconductor microstructures. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Fu-Sheng%22">Yang, Fu-Sheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Liang-Chia%22">Chen, Liang-Chia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lchen@ntu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Ho%2C+Chao-Ching%22">Ho, Chao-Ching</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Advanced+Manufacturing+Technology%22">International Journal of Advanced Manufacturing Technology</searchLink>. Feb2026, Vol. 142 Issue 7/8, p3499-3525. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+diffraction%22">Optical diffraction</searchLink><br /><searchLink fieldCode="DE" term="%22Semiconductor+manufacturing%22">Semiconductor manufacturing</searchLink><br /><searchLink fieldCode="DE" term="%22Metrology%22">Metrology</searchLink><br /><searchLink fieldCode="DE" term="%22Microelectronic+packaging%22">Microelectronic packaging</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study presents an optical critical dimension (CD) measurement technique that integrates artificial neural networks (ANNs) with coherent optical scatterometry (COS) and back focal plane (BFP) imaging for three-dimensional (3D) advanced semiconductor packaging. The proposed method enables simultaneous measurement of line width, line spacing, and height in redistribution layer (RDL) structures by analyzing diffraction signatures from periodic structures. Compared with atomic force microscopy (AFM) and scanning electron microscopy (SEM), which typically require several minutes per measurement, the proposed approach achieves measurement times of less than 3 ms, corresponding to a two-order-of-magnitude improvement in throughput. In comparison to white-light interferometry and confocal microscopy, the method offers approximately a fivefold improvement in lateral resolution, accompanied by significantly higher measurement speed. Experimental validation against AFM demonstrates measurement biases below 2% for line width and spacing and below 1.2% for height. The simple optical configuration also results in lower implementation costs. These characteristics make the proposed method suitable for inline process control in RDL fabrication and 3D IC manufacturing. Current limitations include a restricted depth measurement range and reliance on finite-difference time-domain (FDTD) simulations, which will be addressed in future work. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature 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: 10.1007/s00170-025-17343-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 3499 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Optical diffraction Type: general – SubjectFull: Semiconductor manufacturing Type: general – SubjectFull: Metrology Type: general – SubjectFull: Microelectronic packaging Type: general Titles: – TitleFull: Artificial neural network-driven diffraction imaging for nanoscale optical critical dimension metrology in semiconductor microstructures. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Fu-Sheng – PersonEntity: Name: NameFull: Chen, Liang-Chia – PersonEntity: Name: NameFull: Ho, Chao-Ching IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02683768 Numbering: – Type: volume Value: 142 – Type: issue Value: 7/8 Titles: – TitleFull: International Journal of Advanced Manufacturing Technology Type: main |
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