{"id":"W3097021646","doi":"10.1145/3416946","title":"Efficient Parasitic-aware g m / I D - based Hybrid Sizing Methodology for Analog and RF Integrated Circuits","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Design Automation of Electronic Systems","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Floorplan; Parasitic extraction; Sizing; Computer science; Integrated circuit; Piecewise; Electronic engineering; Analogue electronics; Nonlinear programming; Electronic circuit; Mathematical optimization; Algorithm; Nonlinear system; Electrical engineering; Mathematics; Embedded system; Engineering; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001070119,0.0004556309,0.0007540786,0.0003897326,0.0001966978,0.00007742274,0.0003823159,0.0002672758,0.00003316709],"category_scores_gemma":[0.0001830735,0.0004733101,0.0002032359,0.0006469336,0.00007371627,0.0001455093,0.000003572149,0.0004309753,0.00001305328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003178318,"about_ca_system_score_gemma":0.0001983496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003177932,"about_ca_topic_score_gemma":0.000001436862,"domain_scores_codex":[0.997058,0.0005317142,0.0008741976,0.0005326719,0.0003173843,0.0006859885],"domain_scores_gemma":[0.9973181,0.001615287,0.0001733591,0.0004977755,0.0002095645,0.0001858953],"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":[0.0000619374,0.00006979774,0.000004426479,0.0004807582,0.0002014946,0.00000318196,0.0005636985,0.9607652,0.02504845,0.0001813414,0.0005702391,0.01204951],"study_design_scores_gemma":[0.0009390696,0.0008273212,0.000003740451,0.0001787917,0.0001718157,0.00004242776,0.0003213231,0.8931054,0.1033599,0.0001612675,0.0005178925,0.0003710543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01642274,0.0008391017,0.9790002,0.0002051557,0.00008687699,0.001802615,0.0001341809,0.001459578,0.00004951534],"genre_scores_gemma":[0.9838037,0.00006395198,0.01511894,0.0001686427,0.00006258679,0.0005951255,0.00006465203,0.0001022551,0.00002012601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.967381,"threshold_uncertainty_score":0.9997718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04396500316172459,"score_gpt":0.2605274146914718,"score_spread":0.2165624115297472,"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."}}