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Detection and correlation of yield loss induced by color resist deposition deviation with a deep learning approach applied to optical acquisitions

Abstract : On imager devices, color resists are used as optical filters to produce RGB pixel arrays. These layers are deposited through spin coating process towards the end of the fabrication process flow, where complex topography can induce thickness inhomogeneity effect over the wafer surface causing a radial striations signature, predominant at the edge of the wafer. This deviation can induce important yield loss but is hardly detectable with standard inline metrology or defectivity approach. In this study, an interferometry-based metrology system and a reflectometry-based defectivity system were used to gather raw optical responses on the full wafer surface. Individual die cartographies were created from those and a deep learning algorithm was trained from both optical techniques. We then applied the deep learning algorithm on a specific set of test wafers to determine the number of dies affected by striations. From there, we evaluated the correlation of the outcome classification with the final electrical tests done on each die of those wafers.
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https://hal-cnrs.archives-ouvertes.fr/hal-03368015
Contributor : Jean-Hervé Tortai Connect in order to contact the contributor
Submitted on : Monday, October 11, 2021 - 10:30:42 AM
Last modification on : Tuesday, October 19, 2021 - 11:06:21 AM

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Thomas Alcaire, Delphine Le Cunff, Jean-Hervé Tortai, Sebastien Soulan, Victor Gredy, et al.. Detection and correlation of yield loss induced by color resist deposition deviation with a deep learning approach applied to optical acquisitions. SPIE Advanced Lithography, Feb 2021, Online Only, France. pp.79, ⟨10.1117/12.2582058⟩. ⟨hal-03368015⟩

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