Determination of lapping parameters for silicon wafer using an artificial neural network

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dc.authoridKayabasi, Erhan/0000-0002-3603-6211
dc.authoridOZTURK, Savas/0000-0003-2661-4556
dc.contributor.authorOzturk, Savas
dc.contributor.authorKayabasi, Erhan
dc.contributor.authorCelik, Erdal
dc.contributor.authorKurt, Huseyin
dc.date.accessioned2024-09-29T15:51:15Z
dc.date.available2024-09-29T15:51:15Z
dc.date.issued2018
dc.departmentKarabük Üniversitesien_US
dc.description.abstractAn artificial neural network (ANN) simulation was utilized to determine the lapping parameters such as rotation speed, lapping duration and lapping pressure under a constant slurry supply for n-type crystalline Silicon (c-Si) wafers. Experiments were done with a Logitech PM5 lapping and polishing machine to obtain input data and target data for training, testing and validation of ANN. Lapping operation had five main parameters affecting surface quality: rotation speed, lapping duration, lapping pressure, flowrate of abrasive slurry and particle size in abrasive slurry. However, in this study slurry flowrate was assumed constant due the researches performed before. 218 lapping operations were performed with different values of the selected parameters and new lapping parameters were derived for different lapping conditions to achieve the best surface quality by using an ANN. In this study, wafers in 400 A mu m thickness cut under identical conditions from n-type single c-Si ingot in a STX 1202 DWS cutting machine were employed. Surface roughness (R (a) ) values were measured three times from different points of the wafers after lapping with a contact type surface roughness measurement tool using a microscopic scale stylus profiler (SP). In ANN simulation 70% of R (a) values were utilized for training, 15% of R (a) values were utilized for validation and 15% of R (a) values were utilized for test data. Results obtained from ANN simulation validated with a success above 99%.en_US
dc.description.sponsorshipScientific Research Projects Coordination Unit of Karabuk University [KBU-BAP-14/1-DR-003]; Electronic Materials Production and Application Center (EMUM) at Dokuz Eylul University, Turkeyen_US
dc.description.sponsorshipThis experimental research was supported by the Scientific Research Projects Coordination Unit of Karabuk University (Project Number KBU-BAP-14/1-DR-003) and Electronic Materials Production and Application Center (EMUM) at Dokuz Eylul University, Turkey.en_US
dc.identifier.doi10.1007/s10854-017-7912-4
dc.identifier.endpage270en_US
dc.identifier.issn0957-4522
dc.identifier.issn1573-482X
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85030308120en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage260en_US
dc.identifier.urihttps://doi.org/10.1007/s10854-017-7912-4
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3978
dc.identifier.volume29en_US
dc.identifier.wosWOS:000419363800031en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Materials Science-Materials in Electronicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFinite-Element-Analysisen_US
dc.subjectDielectric-Constanten_US
dc.subjectSurface-Roughnessen_US
dc.subjectSolar-Cellen_US
dc.subjectPerformanceen_US
dc.subjectRemovalen_US
dc.subjectWearen_US
dc.titleDetermination of lapping parameters for silicon wafer using an artificial neural networken_US
dc.typeArticleen_US

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