{"id":"W2103769555","doi":"10.1109/smic.2005.1587951","title":"Modeling and extraction of SiGe HBT noise parameters from measured Y-parameters and accounting for noise correlation","year":2006,"lang":"en","type":"article","venue":"","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Heterojunction bipolar transistor; Noise (video); Correlation; Noise measurement; Computer science; Electrical engineering; Mathematics; Noise reduction; Engineering; Artificial intelligence; Transistor","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":[0.0003601762,0.0009787485,0.000540842,0.000502718,0.0002946805,0.0006812078,0.0008389857,0.0008354616,0.001029442],"category_scores_gemma":[0.001370125,0.0006580148,0.0008321865,0.0006232627,0.0002641575,0.001246413,0.0003647658,0.0005705212,0.000786759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006398478,"about_ca_system_score_gemma":0.001010837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003994717,"about_ca_topic_score_gemma":0.005714034,"domain_scores_codex":[0.999652,0.00004395367,0.00001808803,0.00006697764,0.0001930492,0.00002588066],"domain_scores_gemma":[0.9997399,0.00009344995,0.0000406547,0.00004979047,0.0000709839,0.000005127679],"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":[0.0001458256,0.00005801138,0.003148068,0.0002230862,0.00009186549,0.0003205375,0.000248221,0.7158178,0.2031633,0.007771347,0.0008910997,0.06812079],"study_design_scores_gemma":[0.00001015798,0.0000663292,0.001435436,0.00001424467,0.00003190052,0.000153311,0.00001587862,0.930927,0.06364274,0.00107916,0.002596751,0.00002694375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05315589,0.0002270969,0.9424239,0.0000638406,0.00002613071,0.000066443,0.0002221026,0.001386239,0.002428333],"genre_scores_gemma":[0.670959,0.0009129318,0.3160726,0.00007982064,0.00003646841,0.0004695952,0.0009723866,0.0005933087,0.009903914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003994717,"threshold_uncertainty_score":0.007942915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02023769479967834,"score_gpt":0.2157990491598089,"score_spread":0.1955613543601305,"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."}}