{"id":"W1590025159","doi":"10.1109/icm.2001.997647","title":"Regeneration techniques for RLC VLSI interconnects","year":2001,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"RLC circuit; Inductance; Very-large-scale integration; Interconnection; Regeneration (biology); Electronic engineering; Chip; Series and parallel circuits; Computer science; Electrical engineering; Engineering; Capacitor; Voltage; Telecommunications","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":[0.0001809021,0.0002486008,0.0001861262,0.0004950844,0.0004497926,0.0002143872,0.0004261662,0.0002798426,0.001960062],"category_scores_gemma":[0.0006125143,0.0001642816,0.0002115612,0.0004035785,0.0004749754,0.0005634865,0.0003825805,0.0003280512,0.0007510533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000355245,"about_ca_system_score_gemma":0.0002405477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005141495,"about_ca_topic_score_gemma":0.001010607,"domain_scores_codex":[0.9998521,0.00002284178,0.000008807784,0.00002307385,0.0000718761,0.00002125239],"domain_scores_gemma":[0.9996513,0.00008263864,0.00008047151,0.0001001201,0.00007362843,0.0000118123],"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":[0.0002292085,0.00006262885,0.0006890331,0.0004370733,0.0000226661,0.0002937382,0.0003820186,0.01715391,0.6920744,0.01594721,0.002065676,0.2706424],"study_design_scores_gemma":[0.00006518905,0.0008643863,0.001481873,0.00006461713,0.00007025572,0.00313614,0.0001702149,0.1070769,0.8087696,0.01182604,0.06641856,0.00005624516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2700122,0.003384873,0.6934376,0.0005691764,0.000157968,0.0001891279,0.0001241246,0.006168337,0.02595655],"genre_scores_gemma":[0.8459737,0.0006671355,0.143167,0.0001381844,0.00003911863,0.00006895409,0.0001177287,0.0001330048,0.009695217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001960062,"threshold_uncertainty_score":0.006557107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01143421815261505,"score_gpt":0.2182321019143676,"score_spread":0.2067978837617525,"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."}}