Deep Image Segmentation for Defect Detection in Photo-lithography Fabrication
dc.contributor.author | Paul, Omari | |
dc.contributor.author | Abrar, Sakib | |
dc.contributor.author | Mu, Richard | |
dc.contributor.author | Islam, Riadul | |
dc.contributor.author | Samad, Manar D. | |
dc.date.accessioned | 2025-04-23T20:31:17Z | |
dc.date.available | 2025-04-23T20:31:17Z | |
dc.date.issued | 2023-05-24 | |
dc.description | 2023 24th International Symposium on Quality Electronic Design (ISQED),05-07 April 2023, San Francisco, CA, USA | |
dc.description.abstract | Surface 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.sponsorship | This 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.uri | https://ieeexplore.ieee.org/abstract/document/10129372 | |
dc.format.extent | 7 pages | |
dc.genre | conference papers and proceedings | |
dc.genre | postprints | |
dc.identifier | doi:10.13016/m2tbx1-18yp | |
dc.identifier.citation | Paul, 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.uri | https://doi.org/10.1109/ISQED57927.2023.10129372 | |
dc.identifier.uri | http://hdl.handle.net/11603/38036 | |
dc.language.iso | en_US | |
dc.publisher | IEEE | |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Faculty Collection | |
dc.relation.ispartof | UMBC 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.subject | Image segmentation | |
dc.subject | Machine learning | |
dc.subject | surface acoustic wave sensor | |
dc.subject | defect detection | |
dc.subject | image segmentation | |
dc.subject | convolutional neural network | |
dc.subject | Fabrication | |
dc.subject | Inspection | |
dc.subject | Surface acoustic waves | |
dc.subject | UMBC Cybersecurity Institute | |
dc.subject | photo-lithography | |
dc.subject | autoencoder | |
dc.subject | Visualization | |
dc.subject | Image sensors | |
dc.title | Deep Image Segmentation for Defect Detection in Photo-lithography Fabrication | |
dc.type | Text | |
dcterms.creator | https://orcid.org/0000-0002-4649-3467 |
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