{"id":"W3097021646","doi":"10.1145/3416946","title":"Efficient Parasitic-aware <i> g <sup>m</sup> </i> / <i> I <sup>D</sup> - </i> 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002547777,0.0006280949,0.0002945867,0.0004693705,0.0002373294,0.0004992763,0.0008885975,0.0003494283,0.001744187],"category_scores_gemma":[0.0005155944,0.0002325218,0.0004666018,0.0004989419,0.0002792679,0.0006752389,0.0005468545,0.0004731182,0.0004040104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005239475,"about_ca_system_score_gemma":0.0005798062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008091203,"about_ca_topic_score_gemma":0.001850134,"domain_scores_codex":[0.9998114,0.00003915384,0.000008924308,0.00002884359,0.00009457017,0.00001725491],"domain_scores_gemma":[0.9997873,0.00008292352,0.0000381139,0.00004448734,0.00003790398,0.000009235304],"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":[0.00005038389,0.0000522221,0.0007699819,0.0001747478,0.00004089019,0.00009535449,0.0001036124,0.6944602,0.0962189,0.02297737,0.001860403,0.1831959],"study_design_scores_gemma":[0.000005410823,0.00005295095,0.0001391565,0.000007348724,0.00001128892,0.00008424218,0.00001507478,0.9758296,0.01673031,0.003960484,0.003158021,0.000006132575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01878635,0.0002344136,0.9746844,0.00006596892,0.00001549646,0.00003487345,0.00004767068,0.0005530911,0.005577819],"genre_scores_gemma":[0.4048885,0.0002192381,0.5922341,0.000066322,0.00001594122,0.00007756179,0.0001493321,0.000213601,0.002135475],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001744187,"threshold_uncertainty_score":0.005834877,"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."}}