{"id":"W3094241140","doi":"10.1109/epeps48591.2020.9231491","title":"An Efficient and Parallel Electromagnetic Solver for Complex Interconnects in Layered Media","year":2020,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Solver; Computer science; Scalability; Computation; Computational science; Parallel computing; Workload; Transmission-line matrix method; Computational electromagnetics; Electronic engineering; Algorithm; Electromagnetic field; Engineering; Physics","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":[0.0002392934,0.0004431923,0.0003355207,0.0002313518,0.0003154911,0.0004634641,0.0009105669,0.0005155983,0.00185391],"category_scores_gemma":[0.0006666076,0.0002304968,0.0003733522,0.0003921317,0.0002557947,0.0005343871,0.0005676278,0.0006290274,0.0004474308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002256533,"about_ca_system_score_gemma":0.0007394499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001082201,"about_ca_topic_score_gemma":0.002023719,"domain_scores_codex":[0.9999012,0.00001958754,0.000004126667,0.000009594092,0.00005310661,0.00001235358],"domain_scores_gemma":[0.9997943,0.00009047724,0.00002257059,0.00002592261,0.00004950062,0.00001732669],"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":[0.00007859411,0.00007825119,0.00105114,0.0002132774,0.00005488637,0.0002549822,0.0001150418,0.8103532,0.06032648,0.03056146,0.002936664,0.09397613],"study_design_scores_gemma":[0.000008154552,0.00001507969,0.00004776466,0.000002668322,0.000003035271,0.00003165616,0.00000823098,0.9950646,0.002645105,0.001036609,0.0011343,0.000002760087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01025868,0.00006090063,0.9866446,0.00006006508,0.0000232451,0.00002723975,0.00004672723,0.0004172724,0.002461333],"genre_scores_gemma":[0.1871936,0.0002094882,0.8081808,0.00006319354,0.00002812813,0.0001275984,0.0001367627,0.0002445333,0.003815903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00185391,"threshold_uncertainty_score":0.006201982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02807108252980117,"score_gpt":0.2324803303000332,"score_spread":0.204409247770232,"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."}}