Deep Image Segmentation for Defect Detection in Photo-lithography Fabrication

dc.contributor.authorPaul, Omari
dc.contributor.authorAbrar, Sakib
dc.contributor.authorMu, Richard
dc.contributor.authorIslam, Riadul
dc.contributor.authorSamad, Manar D.
dc.date.accessioned2025-04-23T20:31:17Z
dc.date.available2025-04-23T20:31:17Z
dc.date.issued2023-05-24
dc.description2023 24th International Symposium on Quality Electronic Design (ISQED),05-07 April 2023, San Francisco, CA, USA
dc.description.abstractSurface acoustic wave (SAW) sensors with increasingly unique and refined designed patterns are often developed using the lithographic fabrication processes. Emerging applications of SAW sensors often require novel materials, which may present uncharted fabrication outcomes. The fidelity of the SAW sensor performance is often correlated with the ability to restrict the presence of defects in post-fabrication. Therefore, it is critical to have effective means to detect the presence of defects within the SAW sensor. However, labor-intensive manual labeling is often required due to the need for precision identification and classification of surface features for increased confidence in model accuracy. One approach to automating defect detection is to leverage effective machine learning techniques to analyze and quantify defects within the SAW sensor. In this paper, we propose a machine learning approach using a deep convolutional autoencoder to segment surface features semantically. The proposed deep image autoencoder takes a grayscale input image and generates a color image segmenting the defect region in red, metallic interdigital transducing (IDT) fingers in green, and the substrate region in blue. Experimental results demonstrate promising segmentation scores in locating the defects and regions of interest for a novel SAW sensor variant. The proposed method can automate the process of localizing and measuring post-fabrication defects at the pixel level that may be missed by error-prone visual inspection.
dc.description.sponsorshipThis work was funded by a PREM Grant #N0014-17-1- 3060. The authors would like to acknowledge the contributing Tennessee State University graduate students: Akinwunmi Joaquim and April Falconer.
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/10129372
dc.format.extent7 pages
dc.genreconference papers and proceedings
dc.genrepostprints
dc.identifierdoi:10.13016/m2tbx1-18yp
dc.identifier.citationPaul, Omari, Sakib Abrar, Richard Mu, Riadul Islam, and Manar D. Samad. “Deep Image Segmentation for Defect Detection in Photo-Lithography Fabrication.” 2023 24th International Symposium on Quality Electronic Design (ISQED), April 2023, 1–7. https://doi.org/10.1109/ISQED57927.2023.10129372.
dc.identifier.urihttps://doi.org/10.1109/ISQED57927.2023.10129372
dc.identifier.urihttp://hdl.handle.net/11603/38036
dc.language.isoen_US
dc.publisherIEEE
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.rights© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectImage segmentation
dc.subjectMachine learning
dc.subjectsurface acoustic wave sensor
dc.subjectdefect detection
dc.subjectimage segmentation
dc.subjectconvolutional neural network
dc.subjectFabrication
dc.subjectInspection
dc.subjectSurface acoustic waves
dc.subjectUMBC Cybersecurity Institute
dc.subjectphoto-lithography
dc.subjectautoencoder
dc.subjectVisualization
dc.subjectImage sensors
dc.titleDeep Image Segmentation for Defect Detection in Photo-lithography Fabrication
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-4649-3467

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