{"id":"W4251855355","doi":"10.1109/aspdac.2010.5419880","title":"A performance-constrained template-based layout retargeting algorithm for analog integrated circuits","year":2010,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Retargeting; Parasitic extraction; Computer science; Integrated circuit layout; Algorithm; Set (abstract data type); Analogue electronics; Integer programming; Standard cell; Electronic circuit; IC layout editor; Design layout record; Circuit extraction; Integrated circuit; Electronic engineering; Engineering; Artificial intelligence; Equivalent circuit","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003426802,0.0006811951,0.0004159496,0.0004213905,0.0002320552,0.0003646172,0.0009023809,0.0004580489,0.001769018],"category_scores_gemma":[0.001190259,0.0003329694,0.0004538327,0.0004504441,0.0003082123,0.0005276091,0.0005094837,0.0006503596,0.0005229922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005866874,"about_ca_system_score_gemma":0.0007080501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001750326,"about_ca_topic_score_gemma":0.002514244,"domain_scores_codex":[0.999705,0.00004902851,0.000012544,0.00007095096,0.0001378635,0.00002452018],"domain_scores_gemma":[0.9995908,0.0001845302,0.00006059471,0.00007499794,0.00007586289,0.0000131342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008639521,0.00004594563,0.000325365,0.00008169537,0.00003990143,0.00006736099,0.00006671839,0.5014948,0.08477744,0.006906194,0.001758421,0.4043498],"study_design_scores_gemma":[0.00001300088,0.00005606419,0.0001572155,0.00000365642,0.00001163358,0.00005945977,0.000004640389,0.9749809,0.02077269,0.002072364,0.001858496,0.000009982411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006062666,0.00004362789,0.9921007,0.00002187082,0.000007160884,0.00001969646,0.00002007762,0.001072736,0.0006514502],"genre_scores_gemma":[0.1883165,0.00009289576,0.8089944,0.00005529197,0.00001460695,0.0000943439,0.0001616826,0.0003240543,0.001946198],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001769018,"threshold_uncertainty_score":0.005917907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01136956516990093,"score_gpt":0.2124176745749226,"score_spread":0.2010481094050216,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}