{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105225"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105225","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Optical imaging with machine learning for the automated characterization of micro- and nanoscale devices","abstract":"This dissertation presents techniques and applications associated with enhancing the sensitivity of the optical images in nanotechnology. First, the dissertation presents a novel interpretable machine learning technique for defect detection and classification in noisy optical images of semiconductor wafer die. This solution is designed to solve the imbalanced data-set classification problem for noisy images with some feature similarities in the different classes. A baseline comparison is performed by using a standard technique for defect detection and classification. Secondly, a standard loss function is modified and combined with some portions of the new technique to implement defect detection and classification in optical images of semiconductor wafer die. The performances of both the techniques are compared. An optimization technique is presented for a specific category of functions encountered in this dissertation and the algorithm's performance is compared with standard optimization algorithm(s). Two phase retrieval techniques are compared: diffraction phase microscopy (DPM) and transport of intensity (TIE). TIE is modified for low intensity and zero intensity images. Modified TIE is optimized for theoretical analysis of Gaussian mode phase retrieval. Experimental trials are conducted and optimized hyperparameters are used for phase retrieval.","abstract_html":"This dissertation presents techniques and applications associated with enhancing the sensitivity of the optical images in nanotechnology. First, the dissertation presents a novel interpretable machine learning technique for defect detection and classification in noisy optical images of semiconductor wafer die. This solution is designed to solve the imbalanced data-set classification problem for noisy images with some feature similarities in the different classes. A baseline comparison is performed by using a standard technique for defect detection and classification. Secondly, a standard loss function is modified and combined with some portions of the new technique to implement defect detection and classification in optical images of semiconductor wafer die. The performances of both the techniques are compared. An optimization technique is presented for a specific category of functions encountered in this dissertation and the algorithm's performance is compared with standard optimization algorithm(s). Two phase retrieval techniques are compared: diffraction phase microscopy (DPM) and transport of intensity (TIE). TIE is modified for low intensity and zero intensity images. Modified TIE is optimized for theoretical analysis of Gaussian mode phase retrieval. Experimental trials are conducted and optimized hyperparameters are used for phase retrieval.","abstract_has_math":false,"creators":["Purandare, Sanyogita"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Goddard, Lynford","Choquette, Kent","Koyejo, Oluwasanmi","Popescu, Gabriel","Schwing, Alexander"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:48:19Z","date_published":"2019-08-23T20:48:19Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Machine Learning","Optical Imaging","Optical Image Processing"],"languages":["en"],"rights":["Copyright 2019 Sanyogita Purandare"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105225","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Goddard, Lynford","Choquette, Kent","Koyejo, Oluwasanmi","Popescu, Gabriel","Schwing, Alexander"]},{"key":"dc:creator","label":"Author","values":["Purandare, Sanyogita"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:48:19Z","2021-08-24T09:15:20Z","2019-04-18","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Optical Imaging","Optical Image Processing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Sanyogita Purandare"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105225"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This dissertation presents techniques and applications associated with enhancing the sensitivity of the optical images in nanotechnology. First, the dissertation presents a novel interpretable machine learning technique for defect detection and classification in noisy optical images of semiconductor wafer die. This solution is designed to solve the imbalanced data-set classification problem for noisy images with some feature similarities in the different classes. A baseline comparison is performed by using a standard technique for defect detection and classification. Secondly, a standard loss function is modified and combined with some portions of the new technique to implement defect detection and classification in optical images of semiconductor wafer die. The performances of both the techniques are compared. An optimization technique is presented for a specific category of functions encountered in this dissertation and the algorithm's performance is compared with standard optimization algorithm(s). Two phase retrieval techniques are compared: diffraction phase microscopy (DPM) and transport of intensity (TIE). TIE is modified for low intensity and zero intensity images. Modified TIE is optimized for theoretical analysis of Gaussian mode phase retrieval. Experimental trials are conducted and optimized hyperparameters are used for phase retrieval.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Sanyogita Purandare, accepted the attached license on 2019-04-18 at 14:19.","The student, Sanyogita Purandare, submitted this Dissertation for approval on 2019-04-18 at 14:37.","This Dissertation was approved for publication on 2019-04-18 at 17:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13751 on 2019-08-22 at 16:23:12","Made available in DSpace on 2019-08-23T20:48:19Z (GMT). No. of bitstreams: 3 PURANDARE-DISSERTATION-2019.pdf: 22351151 bytes, checksum: ec0e354737e6ba74ba6b7a5ab531ecd6 (MD5) LICENSE.txt: 4216 bytes, checksum: 599affcd1f4b4f7f15e0fceb71306426 (MD5) PROQUEST_LICENSE.txt: 4562 bytes, checksum: 60b1229a8396f392dbc42838317954c0 (MD5) Previous issue date: 2019-04-18","Embargo set by: Seth Robbins for item 112347 Lift date: 2021-08-23T20:48:32Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 112347 on 2021-08-24T09:15:20Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Optical imaging with machine learning for the automated characterization of micro- and nanoscale devices"]}]}],"canonical_facts":{"dc:contributor":["Goddard, Lynford","Choquette, Kent","Koyejo, Oluwasanmi","Popescu, Gabriel","Schwing, Alexander"],"dc:creator":["Purandare, Sanyogita"],"dc:date":["2019-08-23T20:48:19Z","2021-08-24T09:15:20Z","2019-04-18","2019-05"],"dc:description":["This dissertation presents techniques and applications associated with enhancing the sensitivity of the optical images in nanotechnology. First, the dissertation presents a novel interpretable machine learning technique for defect detection and classification in noisy optical images of semiconductor wafer die. This solution is designed to solve the imbalanced data-set classification problem for noisy images with some feature similarities in the different classes. A baseline comparison is performed by using a standard technique for defect detection and classification. Secondly, a standard loss function is modified and combined with some portions of the new technique to implement defect detection and classification in optical images of semiconductor wafer die. The performances of both the techniques are compared. An optimization technique is presented for a specific category of functions encountered in this dissertation and the algorithm's performance is compared with standard optimization algorithm(s). Two phase retrieval techniques are compared: diffraction phase microscopy (DPM) and transport of intensity (TIE). TIE is modified for low intensity and zero intensity images. Modified TIE is optimized for theoretical analysis of Gaussian mode phase retrieval. Experimental trials are conducted and optimized hyperparameters are used for phase retrieval.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Sanyogita Purandare, accepted the attached license on 2019-04-18 at 14:19.","The student, Sanyogita Purandare, submitted this Dissertation for approval on 2019-04-18 at 14:37.","This Dissertation was approved for publication on 2019-04-18 at 17:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13751 on 2019-08-22 at 16:23:12","Made available in DSpace on 2019-08-23T20:48:19Z (GMT). No. of bitstreams: 3 PURANDARE-DISSERTATION-2019.pdf: 22351151 bytes, checksum: ec0e354737e6ba74ba6b7a5ab531ecd6 (MD5) LICENSE.txt: 4216 bytes, checksum: 599affcd1f4b4f7f15e0fceb71306426 (MD5) PROQUEST_LICENSE.txt: 4562 bytes, checksum: 60b1229a8396f392dbc42838317954c0 (MD5) Previous issue date: 2019-04-18","Embargo set by: Seth Robbins for item 112347 Lift date: 2021-08-23T20:48:32Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 112347 on 2021-08-24T09:15:20Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/105225"],"dc:language":["en"],"dc:rights":["Copyright 2019 Sanyogita Purandare"],"dc:subject":["Machine Learning","Optical Imaging","Optical Image Processing"],"dc:title":["Optical imaging with machine learning for the automated characterization of micro- and nanoscale devices"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:44Z"}