{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/102490"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/102490","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Nondestructive photoelastic characterization of monocrystalline silicon photovoltaic wafers","abstract":"Silicon wafers produced for the photovoltaic industry are becoming thinner. This renders the wafers more susceptible to bow under thermal residual and wafer handling stresses. Furthermore, a saw damage layer exists on the top and bottom surface of each wafer as a result of slicing the silicon ingot into wafers via a wire saw. Thinner wafers are more prone fracture under the combination of residual stress and external loads acting on microcracks in the saw damage layer. This research presents a novel method for characterizing monocrystalline silicon wafers using a photoelastic imaging technique that is capable of measuring the bulk residual stress fields in silicon wafers as well as locally elevated stress fields near microcracks. Infrared photoelastic (PE) imaging is used to reveal the bulk residual stress field of each wafer and locally elevated stress field around each defect. The defect stress fields are first classified by using a support vector machine learning algorithm. The measured local and bulk residual stress fields around each defect are analyzed in order to characterize each crack according to its stress intensity factors. The distribution of cracks on each wafer is subjected to a virtual uniaxial tensile load where the wafer strength is determined by the load at which the first crack is expected to propagate. The distribution of wafer strengths is used to calculate the expected Weibull parameters for the wafer set, under a pure tensile load. Since the method is nondestructive, it can be used to calculate and compare the expected Weibull parameters for the same set of wafers under a variety of externally applied loads. The variation in wafer strength when loaded parallel and perpendicular to the direction of wire motion is in agreement with experimental observations. With the proper modifications to the experimental setup, using this method it is possible to rapidly and accurately characterize large batches of wafers in an automated way.","abstract_html":"Silicon wafers produced for the photovoltaic industry are becoming thinner. This renders the wafers more susceptible to bow under thermal residual and wafer handling stresses. Furthermore, a saw damage layer exists on the top and bottom surface of each wafer as a result of slicing the silicon ingot into wafers via a wire saw. Thinner wafers are more prone fracture under the combination of residual stress and external loads acting on microcracks in the saw damage layer. This research presents a novel method for characterizing monocrystalline silicon wafers using a photoelastic imaging technique that is capable of measuring the bulk residual stress fields in silicon wafers as well as locally elevated stress fields near microcracks. Infrared photoelastic (PE) imaging is used to reveal the bulk residual stress field of each wafer and locally elevated stress field around each defect. The defect stress fields are first classified by using a support vector machine learning algorithm. The measured local and bulk residual stress fields around each defect are analyzed in order to characterize each crack according to its stress intensity factors. The distribution of cracks on each wafer is subjected to a virtual uniaxial tensile load where the wafer strength is determined by the load at which the first crack is expected to propagate. The distribution of wafer strengths is used to calculate the expected Weibull parameters for the wafer set, under a pure tensile load. Since the method is nondestructive, it can be used to calculate and compare the expected Weibull parameters for the same set of wafers under a variety of externally applied loads. The variation in wafer strength when loaded parallel and perpendicular to the direction of wire motion is in agreement with experimental observations. With the proper modifications to the experimental setup, using this method it is possible to rapidly and accurately characterize large batches of wafers in an automated way.","abstract_has_math":false,"creators":["Rowe, Logan Perris"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Johnson, Harley T.","Horn, Gavin P."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-02-06T19:36:37Z","date_published":"2019-02-06T19:36:37Z","updated_at":"2026-07-22T22:24:42Z","subjects":["photoelasticity, photovoltaic, solar, silicon, machine learning, Weibull, microcrack detection"],"languages":["en"],"rights":["Copyright 2018 Logan Rowe"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/102490","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Johnson, Harley T.","Horn, Gavin P."]},{"key":"dc:creator","label":"Author","values":["Rowe, Logan Perris"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-02-06T19:36:37Z","2018-12-07","2018-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["photoelasticity, photovoltaic, solar, silicon, machine learning, Weibull, microcrack detection"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Logan Rowe"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/102490"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Silicon wafers produced for the photovoltaic industry are becoming thinner. This renders the wafers more susceptible to bow under thermal residual and wafer handling stresses. Furthermore, a saw damage layer exists on the top and bottom surface of each wafer as a result of slicing the silicon ingot into wafers via a wire saw. Thinner wafers are more prone fracture under the combination of residual stress and external loads acting on microcracks in the saw damage layer. This research presents a novel method for characterizing monocrystalline silicon wafers using a photoelastic imaging technique that is capable of measuring the bulk residual stress fields in silicon wafers as well as locally elevated stress fields near microcracks. Infrared photoelastic (PE) imaging is used to reveal the bulk residual stress field of each wafer and locally elevated stress field around each defect. The defect stress fields are first classified by using a support vector machine learning algorithm. The measured local and bulk residual stress fields around each defect are analyzed in order to characterize each crack according to its stress intensity factors. The distribution of cracks on each wafer is subjected to a virtual uniaxial tensile load where the wafer strength is determined by the load at which the first crack is expected to propagate. The distribution of wafer strengths is used to calculate the expected Weibull parameters for the wafer set, under a pure tensile load. Since the method is nondestructive, it can be used to calculate and compare the expected Weibull parameters for the same set of wafers under a variety of externally applied loads. The variation in wafer strength when loaded parallel and perpendicular to the direction of wire motion is in agreement with experimental observations. With the proper modifications to the experimental setup, using this method it is possible to rapidly and accurately characterize large batches of wafers in an automated way.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Logan Rowe, accepted the attached license on 2018-12-06 at 11:25.","The student, Logan Rowe, submitted this Thesis for approval on 2018-12-06 at 11:36.","This Thesis was approved for publication on 2018-12-07 at 09:01.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13238 on 2019-02-05 at 11:15:21","Made available in DSpace on 2019-02-06T19:36:37Z (GMT). No. of bitstreams: 2 ROWE-THESIS-2018.pdf: 1457331 bytes, checksum: f7720f272e3fade9dc0c94aaec49d2f0 (MD5) LICENSE.txt: 4207 bytes, checksum: 9cf6828a8dc5c9355af0166aedc57fa4 (MD5) Previous issue date: 2018-12-07"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Nondestructive photoelastic characterization of monocrystalline silicon photovoltaic wafers"]}]}],"canonical_facts":{"dc:contributor":["Johnson, Harley T.","Horn, Gavin P."],"dc:creator":["Rowe, Logan Perris"],"dc:date":["2019-02-06T19:36:37Z","2018-12-07","2018-12"],"dc:description":["Silicon wafers produced for the photovoltaic industry are becoming thinner. This renders the wafers more susceptible to bow under thermal residual and wafer handling stresses. Furthermore, a saw damage layer exists on the top and bottom surface of each wafer as a result of slicing the silicon ingot into wafers via a wire saw. Thinner wafers are more prone fracture under the combination of residual stress and external loads acting on microcracks in the saw damage layer. This research presents a novel method for characterizing monocrystalline silicon wafers using a photoelastic imaging technique that is capable of measuring the bulk residual stress fields in silicon wafers as well as locally elevated stress fields near microcracks. Infrared photoelastic (PE) imaging is used to reveal the bulk residual stress field of each wafer and locally elevated stress field around each defect. The defect stress fields are first classified by using a support vector machine learning algorithm. The measured local and bulk residual stress fields around each defect are analyzed in order to characterize each crack according to its stress intensity factors. The distribution of cracks on each wafer is subjected to a virtual uniaxial tensile load where the wafer strength is determined by the load at which the first crack is expected to propagate. The distribution of wafer strengths is used to calculate the expected Weibull parameters for the wafer set, under a pure tensile load. Since the method is nondestructive, it can be used to calculate and compare the expected Weibull parameters for the same set of wafers under a variety of externally applied loads. The variation in wafer strength when loaded parallel and perpendicular to the direction of wire motion is in agreement with experimental observations. With the proper modifications to the experimental setup, using this method it is possible to rapidly and accurately characterize large batches of wafers in an automated way.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Logan Rowe, accepted the attached license on 2018-12-06 at 11:25.","The student, Logan Rowe, submitted this Thesis for approval on 2018-12-06 at 11:36.","This Thesis was approved for publication on 2018-12-07 at 09:01.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13238 on 2019-02-05 at 11:15:21","Made available in DSpace on 2019-02-06T19:36:37Z (GMT). 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