{"id":"W4388430537","doi":"10.1109/tcsi.2023.3328807","title":"MMSE Equalizer Design Optimization for Wireline SerDes Applications","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Advancements in PLL and VCO Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Jitter; Wireline; Computer science; Noise (video); Sampling (signal processing); Control theory (sociology); Electronic engineering; Filter (signal processing); Engineering; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001675761,0.0001572607,0.0001751801,0.0001690103,0.0002366856,0.00004337617,0.00009454344,0.0001213978,0.00001082848],"category_scores_gemma":[0.000006045754,0.0001490365,0.00005041269,0.0003235879,0.00004870064,0.00009215706,7.035476e-7,0.00008103223,0.00001171138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004300245,"about_ca_system_score_gemma":0.00001070094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002119225,"about_ca_topic_score_gemma":0.000001278689,"domain_scores_codex":[0.9991888,0.00002004641,0.0002332027,0.00021686,0.0001189396,0.0002221617],"domain_scores_gemma":[0.9995239,0.0001170612,0.00003029619,0.0002288013,0.0000390389,0.0000608408],"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":[0.000003191869,0.00001320827,6.631532e-7,0.0001355041,0.00005952479,6.254979e-7,0.00006714954,0.9345894,0.004922604,0.0004534828,0.0003063018,0.05944828],"study_design_scores_gemma":[0.0006314175,0.0001034201,0.000003208447,0.00009500601,0.00006552703,0.00001101403,0.0009430767,0.9628651,0.006992703,0.0002449361,0.02768482,0.000359828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004388298,0.0003192549,0.9963023,0.00005847373,0.0004206922,0.0009185232,0.0000662297,0.001189812,0.0002858519],"genre_scores_gemma":[0.9918465,0.001463999,0.003070376,0.00002616329,0.00006283001,0.001883024,0.00002440652,0.00006616412,0.001556542],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.993232,"threshold_uncertainty_score":0.6077533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03631792767737092,"score_gpt":0.2513528392195509,"score_spread":0.21503491154218,"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."}}