{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86465"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86465","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"On-Chip Real-Time Training of Analog Artificial Neural Network Classifier Circuit for Breast Cancer Classification in 65nm CMOS","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Ramesh, Naveen; 0000-0003-2064-3647"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Sanyal, Arindam","Electrical Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T17:22:42Z","date_published":"2025-02-21T17:22:42Z","updated_at":"2026-07-27T19:05:32Z","subjects":["electrical engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/86465","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sanyal, Arindam","Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Ramesh, Naveen; 0000-0003-2064-3647"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T17:22:42Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["electrical engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/86465"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","An Analog Artificial Neural Network (AANN) classifier using a common-source amplifier based nonlinear activation function is trained using transistor level implementation of backpropagation technique in-order to perform on-chip real-time training of AANN circuit. Training is done in 65nm CMOS to perform binary classification on breast cancer dataset and identify each patient data as either benign or malignant. Achieved an accuracy of 95% for transistor level training. The AANN training circuit is designed in Cadence Virtuoso and validated using Spectre and MATLAB simulations.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["On-Chip Real-Time Training of Analog Artificial Neural Network Classifier Circuit for Breast Cancer Classification in 65nm CMOS"]}]}],"canonical_facts":{"dc:contributor":["Sanyal, Arindam","Electrical Engineering"],"dc:creator":["Ramesh, Naveen; 0000-0003-2064-3647"],"dc:date":["2025-02-21T17:22:42Z","2020"],"dc:description":["M.S.","An Analog Artificial Neural Network (AANN) classifier using a common-source amplifier based nonlinear activation function is trained using transistor level implementation of backpropagation technique in-order to perform on-chip real-time training of AANN circuit. Training is done in 65nm CMOS to perform binary classification on breast cancer dataset and identify each patient data as either benign or malignant. Achieved an accuracy of 95% for transistor level training. The AANN training circuit is designed in Cadence Virtuoso and validated using Spectre and MATLAB simulations.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86465"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["electrical engineering"],"dc:title":["On-Chip Real-Time Training of Analog Artificial Neural Network Classifier Circuit for Breast Cancer Classification in 65nm CMOS"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:32Z"}