{"id":"W3091003631","doi":"10.1109/iscas45731.2020.9181150","title":"Fast Analog Layout Retargeting with Device Abstraction","year":2020,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Retargeting; Computer science; Process (computing); Abstraction; Graph; Constraint (computer-aided design); IC layout editor; Integrated circuit layout; Computer hardware; Scheme (mathematics); Page layout; Artificial intelligence; Integrated circuit; Circuit extraction; Theoretical computer science; Engineering; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002428987,0.0007243556,0.0004609555,0.0004832524,0.0002110444,0.0005355046,0.001336808,0.00034967,0.003743483],"category_scores_gemma":[0.000775001,0.000247112,0.0006120198,0.0004686606,0.0004056362,0.000819077,0.0008674068,0.0006852549,0.0008196317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000399008,"about_ca_system_score_gemma":0.0004072962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008108605,"about_ca_topic_score_gemma":0.001281025,"domain_scores_codex":[0.9995629,0.00005299083,0.00002428775,0.00009972917,0.0002024834,0.00005762065],"domain_scores_gemma":[0.9993476,0.0001717036,0.00006975984,0.0003246509,0.00007060854,0.00001557562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001936496,0.00005741161,0.0007504281,0.0005101957,0.00008692661,0.0006280223,0.0003010618,0.1160679,0.4161568,0.02370612,0.003970302,0.4375713],"study_design_scores_gemma":[0.0000914165,0.0006038077,0.001234939,0.00004340865,0.0001282135,0.001435074,0.00009254103,0.404125,0.5005401,0.01893731,0.07267823,0.00008999665],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02048765,0.0003136909,0.9712801,0.00004820061,0.00005372415,0.00007920977,0.00009792096,0.004316458,0.003323125],"genre_scores_gemma":[0.4426528,0.0003532565,0.5489784,0.0001967634,0.00003586104,0.0001338445,0.0004500452,0.0009429506,0.006256138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003743483,"threshold_uncertainty_score":0.01252317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508758007532245,"score_gpt":0.1893613219481902,"score_spread":0.1742737418728678,"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."}}