{"id":"W2808261674","doi":"10.1109/tvlsi.2018.2839698","title":"Electromigration- and Parasitic-Aware ILP-Based Analog Router","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Copper Interconnects and Reliability","field":"Materials Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Research and Development Corporation of Newfoundland and Labrador; Canada Foundation for Innovation","keywords":"Parasitic extraction; Routing (electronic design automation); Computer science; Electromigration; Router; Integer programming; Interconnection; Electronic engineering; Electronic circuit; Sensitivity (control systems); Radio frequency; Analogue electronics; Computer engineering; Algorithm; Electrical engineering; Engineering; Computer network; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0006292859,0.0003047966,0.0003582973,0.0002043787,0.0005643242,0.0003531841,0.0001950236,0.0002187644,0.0006614499],"category_scores_gemma":[0.00001679731,0.0002396644,0.000146848,0.0002645996,0.0002104915,0.0004422487,0.00000150721,0.0002543548,0.0003632249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001942874,"about_ca_system_score_gemma":0.0001145291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006496585,"about_ca_topic_score_gemma":0.003785089,"domain_scores_codex":[0.99768,0.0002999208,0.00057785,0.0006334743,0.0003789919,0.0004297727],"domain_scores_gemma":[0.9986179,0.0001735858,0.0001339286,0.0005118682,0.0003964941,0.0001662049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007151205,0.001341003,0.0005820129,0.0002713867,0.00009893579,0.00001319965,0.003518696,0.003295298,0.9828603,0.0006184763,0.003559125,0.003126483],"study_design_scores_gemma":[0.001315349,0.00127327,0.0007638823,0.000425594,0.0001098481,0.00004068114,0.001473821,0.2081719,0.7836338,0.0000923107,0.002046183,0.0006533358],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4885587,0.0000411441,0.5083703,0.000253061,0.00192949,0.0003708333,0.0001338677,0.0001451585,0.0001974837],"genre_scores_gemma":[0.9981913,0.00001007995,0.0002671411,0.0003135008,0.0002736546,0.0001452712,0.00002028647,0.00002877129,0.0007499682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5096326,"threshold_uncertainty_score":0.9773232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01074014243960667,"score_gpt":0.2511543537369035,"score_spread":0.2404142112972968,"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."}}