{"id":"W2142473360","doi":"10.1109/mwsym.2005.1516556","title":"Domain Decomposition FDTD Algorithm Combining with Numerical TL Calibration Technique for Parameter Extraction of Substrate Integrated Circuits","year":2005,"lang":"en","type":"article","venue":"IEEE MTT-S International Microwave Symposium Digest, 2005.","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Finite-difference time-domain method; Domain decomposition methods; Calibration; Electronic circuit; Computer science; Algorithm; Extraction (chemistry); Substrate (aquarium); Decomposition; Domain (mathematical analysis); Electronic engineering; Mathematics; Optics; Engineering; Electrical engineering; Physics; Chemistry; Finite element method; Mathematical analysis","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":[0.0004468611,0.0004083794,0.0003272269,0.0004710488,0.0002154708,0.0004155692,0.0004986469,0.0005083518,0.00145907],"category_scores_gemma":[0.001586489,0.000294393,0.0003201191,0.0005882782,0.000291769,0.0007592765,0.0003456627,0.000583343,0.000546435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004049024,"about_ca_system_score_gemma":0.0005659037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008640939,"about_ca_topic_score_gemma":0.0009313928,"domain_scores_codex":[0.9998121,0.00004587349,0.000009858571,0.00002267531,0.0000985396,0.00001092013],"domain_scores_gemma":[0.9995638,0.000174246,0.0000356556,0.00006767888,0.0001497698,0.000008924914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001079178,0.00004713805,0.001170629,0.0001998222,0.00003824382,0.0001188041,0.0002510102,0.3459436,0.115796,0.04020484,0.003332326,0.4927896],"study_design_scores_gemma":[0.0000142578,0.00002212046,0.0001044762,0.000008333191,0.00000604483,0.0000859471,0.00001257227,0.9736419,0.0180494,0.00245974,0.005587479,0.000007700432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002199008,0.0000340254,0.9969465,0.00002267784,0.000009147434,0.000009471572,0.000007137405,0.0002387291,0.000533252],"genre_scores_gemma":[0.069439,0.0001073832,0.9290114,0.00002641059,0.000007339743,0.00007341182,0.00006558401,0.00007128331,0.001198114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00145907,"threshold_uncertainty_score":0.004881024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008767229552085212,"score_gpt":0.2417325123037631,"score_spread":0.2329652827516779,"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."}}