{"id":"W2117886869","doi":"10.1109/modsym.2006.365209","title":"Modeling RF Signal Propagation Along On-Chip Interconnects and the Effect of Substrate Doping with the Alternating-Direction-Implicit Finite-Difference Time-Domain (ADI-FDTD) Method","year":2006,"lang":"en","type":"article","venue":"Proceedings of the International Power Modulator Symposium and High Voltage Workshop/Proceedings of the ... International Power Modulator Symposium and ... High Voltage Workshop","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Finite-difference time-domain method; Substrate (aquarium); Materials science; Alternating direction implicit method; Doping; SIGNAL (programming language); Finite difference method; Optoelectronics; Noise (video); Electronic engineering; Optics; Physics; Computer science; Mathematical analysis; Mathematics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001666253,0.0008427435,0.0009382829,0.0003896393,0.0004755189,0.000476466,0.00164279,0.0003126887,0.00004069936],"category_scores_gemma":[0.0003027078,0.0004860447,0.0003513526,0.0006377337,0.0007095487,0.0005706807,0.0005882673,0.0008930923,0.000001261434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001721497,"about_ca_system_score_gemma":0.00003395383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002688952,"about_ca_topic_score_gemma":0.00002247143,"domain_scores_codex":[0.9956861,0.00005312118,0.001301293,0.0009064485,0.001485924,0.0005670798],"domain_scores_gemma":[0.9961587,0.00152058,0.0009887157,0.0003166926,0.0008590454,0.000156228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002234957,0.0001701356,0.01433711,0.0003346157,0.001082809,0.00000177824,0.001851296,0.09181587,0.8281953,0.05869249,0.0004849219,0.0007986926],"study_design_scores_gemma":[0.005896864,0.00066304,0.01792819,0.002116051,0.0005139838,0.0001404267,0.0007294951,0.7776915,0.1824826,0.01040737,0.0002212337,0.001209304],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834029,0.0002658006,0.007650916,0.00401376,0.001085024,0.001162453,0.00004756802,0.0001331341,0.00223845],"genre_scores_gemma":[0.9972062,0.00007627501,0.001051038,0.0002120937,0.000359345,0.0001226918,0.000009147418,0.000125676,0.0008375403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6858756,"threshold_uncertainty_score":0.9997591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004210794333919807,"score_gpt":0.2083343987062066,"score_spread":0.2041236043722868,"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."}}