{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/69238"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/69238","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Automatic Tuning Algorithms and Statistical Circuit Design","abstract":"In this dissertation two topics are investigated the first of which is automatic tuning algorithms for active filters. Here the problem is that in order to meet response specifications these filters usually must be tuned or adjusted, preferably by computer automation if the production level is high. Three generalized tuning algorithms which have recently appeared in the literature are comparatively reviewed on the basis of their architecture, computational complexity, and effectiveness. Furthermore, a method is presented for the tuning resistor and frequency selection problem, a problem relevant to all three methods. Several statistical simulation examples enhance the presentation. The second topic is statistical circuit design where the emphasis is on Monte Carlo techniques for yield estimation and yield maximization. Several techniques for achieving variance reduction in the yield estimates are discussed. A quadratic approximation model is set up for the circuit and is used to provide an extrapolated yield approximation technique which is extremely effective and efficient in approximating and maximizing the yield along a search direction. Several examples demonstrate the yield maximization process.","abstract_html":"In this dissertation two topics are investigated the first of which is automatic tuning algorithms for active filters. Here the problem is that in order to meet response specifications these filters usually must be tuned or adjusted, preferably by computer automation if the production level is high. Three generalized tuning algorithms which have recently appeared in the literature are comparatively reviewed on the basis of their architecture, computational complexity, and effectiveness. Furthermore, a method is presented for the tuning resistor and frequency selection problem, a problem relevant to all three methods. Several statistical simulation examples enhance the presentation. The second topic is statistical circuit design where the emphasis is on Monte Carlo techniques for yield estimation and yield maximization. Several techniques for achieving variance reduction in the yield estimates are discussed. A quadratic approximation model is set up for the circuit and is used to provide an extrapolated yield approximation technique which is extremely effective and efficient in approximating and maximizing the yield along a search direction. Several examples demonstrate the yield maximization process.","abstract_has_math":false,"creators":["Hocevar, Dale Edward"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-15T19:04:22Z","date_published":"2014-12-15T19:04:22Z","updated_at":"2026-07-22T22:26:00Z","subjects":["Engineering, Electronics and Electrical"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(UMI)AAI8302881"],"render_values":[{"text":"(UMI)AAI8302881","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/69238","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Hocevar, Dale Edward"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-15T19:04:22Z","10000-01-01","1982"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"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":["Engineering, Electronics and Electrical"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/69238","(UMI)AAI8302881"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this dissertation two topics are investigated the first of which is automatic tuning algorithms for active filters. Here the problem is that in order to meet response specifications these filters usually must be tuned or adjusted, preferably by computer automation if the production level is high. Three generalized tuning algorithms which have recently appeared in the literature are comparatively reviewed on the basis of their architecture, computational complexity, and effectiveness. Furthermore, a method is presented for the tuning resistor and frequency selection problem, a problem relevant to all three methods. Several statistical simulation examples enhance the presentation. The second topic is statistical circuit design where the emphasis is on Monte Carlo techniques for yield estimation and yield maximization. Several techniques for achieving variance reduction in the yield estimates are discussed. A quadratic approximation model is set up for the circuit and is used to provide an extrapolated yield approximation technique which is extremely effective and efficient in approximating and maximizing the yield along a search direction. Several examples demonstrate the yield maximization process.","Made available in DSpace on 2014-12-15T19:04:22Z (GMT). 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Here the problem is that in order to meet response specifications these filters usually must be tuned or adjusted, preferably by computer automation if the production level is high. Three generalized tuning algorithms which have recently appeared in the literature are comparatively reviewed on the basis of their architecture, computational complexity, and effectiveness. Furthermore, a method is presented for the tuning resistor and frequency selection problem, a problem relevant to all three methods. Several statistical simulation examples enhance the presentation. The second topic is statistical circuit design where the emphasis is on Monte Carlo techniques for yield estimation and yield maximization. Several techniques for achieving variance reduction in the yield estimates are discussed. A quadratic approximation model is set up for the circuit and is used to provide an extrapolated yield approximation technique which is extremely effective and efficient in approximating and maximizing the yield along a search direction. Several examples demonstrate the yield maximization process.","Made available in DSpace on 2014-12-15T19:04:22Z (GMT). No. of bitstreams: 1 8302881.pdf: 5962677 bytes, checksum: 614c788e859e2502032d57e9b801973a (MD5) Previous issue date: 1982","Embargo set by: Seth Robbins for item 69404 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","223 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1982."],"dc:identifier":["http://hdl.handle.net/2142/69238","(UMI)AAI8302881"],"dc:subject":["Engineering, Electronics and Electrical"],"dc:title":["Automatic Tuning Algorithms and Statistical Circuit Design"],"dc:type":["text"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:00Z"}