{"id":"W2792673958","doi":"10.1109/jssc.2017.2776307","title":"On-Chip Jitter Measurement Using Jitter Injection in a 28 Gb/s PI-Based CDR","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Solid-State Circuits","topic":"Advancements in PLL and VCO Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Tokyo; CMC Microsystems","keywords":"Jitter; Chip; Autocorrelation; Physics; Detector; CMOS; Electronic engineering; Computer science; Optics; Optoelectronics; Telecommunications; Mathematics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000460631,0.0002275373,0.000234587,0.0004125271,0.0002481182,0.0005564412,0.0006458499,0.0003899756,0.0008102835],"category_scores_gemma":[0.001603569,0.0001518819,0.00009510344,0.0004181381,0.0002968784,0.0003724026,0.0003402309,0.0003935137,0.0001933438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006115378,"about_ca_system_score_gemma":0.0006525444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001555748,"about_ca_topic_score_gemma":0.002410806,"domain_scores_codex":[0.9994736,0.00006400913,0.00002166094,0.00009152915,0.0002855058,0.00006363287],"domain_scores_gemma":[0.9990478,0.0003302803,0.000234811,0.0001291729,0.000206796,0.00005110184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003729518,0.0001743751,0.006496652,0.00006597735,0.00002052573,0.0001781542,0.0002388864,0.003919635,0.9621244,0.0005961487,0.0001367624,0.02567554],"study_design_scores_gemma":[0.00003865002,0.00071195,0.007213686,0.00001204258,0.00003402088,0.0002459876,0.00006575217,0.03458118,0.9556756,0.0001206759,0.001271452,0.00002893097],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974835,0.000197672,0.02253254,0.00005120481,0.00002897219,0.00004560244,0.00009228635,0.0005116448,0.001705126],"genre_scores_gemma":[0.9874342,0.00007682696,0.01183405,0.00003696044,0.000006581641,0.00001489285,0.00002914366,0.00002112385,0.0005461948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001555748,"threshold_uncertainty_score":0.004437089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05077987151171821,"score_gpt":0.2831230312757749,"score_spread":0.2323431597640567,"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."}}