{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000261519,0.0005661748,0.0004029069,0.0003961947,0.0002519406,0.0005425137,0.001164084,0.0003721817,0.002092561],"category_scores_gemma":[0.0004191993,0.0002539135,0.0003911285,0.0003119126,0.0002636181,0.0005749596,0.0005319308,0.0003906957,0.0003599559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004982128,"about_ca_system_score_gemma":0.0005374766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000805271,"about_ca_topic_score_gemma":0.001514332,"domain_scores_codex":[0.9997651,0.00005093294,0.00001150869,0.00005077881,0.00009088459,0.00003070734],"domain_scores_gemma":[0.9998047,0.00005780401,0.00004164793,0.0000346726,0.00005021032,0.00001111056],"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":[0.00008476738,0.00007356225,0.0005412098,0.0001505902,0.00004184465,0.0001431943,0.00005424978,0.8286712,0.0493244,0.008968308,0.001926363,0.1100204],"study_design_scores_gemma":[0.000007074575,0.0000419498,0.00006088376,0.000003859898,0.00001471459,0.00003890063,0.000006150373,0.9922671,0.00562354,0.001022812,0.0009081323,0.000004898788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01854585,0.000143309,0.9757506,0.0000787821,0.00002358071,0.00003156092,0.00003072807,0.0006975359,0.004698083],"genre_scores_gemma":[0.5859373,0.0001795607,0.4087518,0.0001476024,0.00002992577,0.00008936947,0.0001101317,0.0001199114,0.004634453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002092561,"threshold_uncertainty_score":0.007000327,"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."}}