{"id":"W3160865949","doi":"10.1109/tcad.2021.3079126","title":"Algorithm Selection Framework for Legalization Using Deep Convolutional Neural Networks and Transfer Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Legalization; Convolutional neural network; Computer science; Transfer of learning; Artificial intelligence; Routing (electronic design automation); Selection (genetic algorithm); Artificial neural network; Deep learning; Algorithm; Machine learning; Displacement (psychology); Embedded system","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":[0.001679592,0.00100668,0.0009777141,0.0012674,0.0004402525,0.0009166827,0.001984282,0.0008660945,0.002524215],"category_scores_gemma":[0.003510339,0.0004302812,0.0007279849,0.0005879827,0.0007168415,0.001575583,0.000818532,0.001300564,0.0006239655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001819201,"about_ca_system_score_gemma":0.001866904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005378269,"about_ca_topic_score_gemma":0.008477981,"domain_scores_codex":[0.9989833,0.0002199425,0.00006168694,0.0002487639,0.0003577912,0.000128562],"domain_scores_gemma":[0.998732,0.0004225021,0.0001872053,0.0002123372,0.0003885954,0.00005740684],"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":[0.000183796,0.0001823781,0.002968888,0.00008187711,0.00006328402,0.0001218677,0.00008455435,0.5937082,0.01135034,0.00985728,0.004416178,0.3769814],"study_design_scores_gemma":[0.000008453798,0.00002735102,0.000089833,0.000002975383,0.00000534417,0.00001418868,0.000004791858,0.9941068,0.003399316,0.001864493,0.0004730182,0.000003471825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04962398,0.0003908889,0.9419112,0.0002329046,0.00004058599,0.0001363394,0.00009688289,0.005239096,0.002328076],"genre_scores_gemma":[0.6169417,0.0002302573,0.3766962,0.0003262012,0.00005256333,0.0002343026,0.0005757153,0.0004869881,0.00445603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005378269,"threshold_uncertainty_score":0.01319933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0413645283119608,"score_gpt":0.2511828270059919,"score_spread":0.2098182986940311,"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."}}