{"id":"W4385337732","doi":"10.1109/ims37964.2023.10188041","title":"Optimization of Decoupling Capacitors in VLSI Systems using Granularity Learning and Logistic Regression based PSO","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Science and Engineering Research Board","keywords":"Decoupling (probability); Granularity; Capacitor; Particle swarm optimization; Decoupling capacitor; Computer science; Very-large-scale integration; Electronic engineering; Control theory (sociology); Engineering; Control engineering; Artificial intelligence; Electrical engineering; Algorithm; Voltage","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":[0.0003188845,0.0004991257,0.0005231026,0.0002154895,0.0001877441,0.0005311967,0.0004510596,0.0005180452,0.001159458],"category_scores_gemma":[0.0009697371,0.0002439638,0.0003332375,0.0004066507,0.0002403918,0.0004060349,0.0003527465,0.000446625,0.0001415202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003484979,"about_ca_system_score_gemma":0.0004969944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002755655,"about_ca_topic_score_gemma":0.002236926,"domain_scores_codex":[0.9998566,0.00003536585,0.000008462286,0.00003054565,0.00004645668,0.00002253077],"domain_scores_gemma":[0.9997903,0.0001147198,0.00003267571,0.00001134267,0.00004157253,0.000009447834],"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":[0.00002464841,0.0000183941,0.0003652625,0.000047565,0.00001757141,0.00003603939,0.00001431575,0.9709798,0.002589599,0.0009851442,0.0003174342,0.0246043],"study_design_scores_gemma":[0.000004504757,0.00001386178,0.00007854021,0.000001505805,0.000002366564,0.000006768778,0.000002944678,0.9993143,0.0002923736,0.0001758834,0.0001054831,0.000001489364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07401207,0.0006210435,0.9194146,0.0002118081,0.00004478753,0.00004683574,0.0000380554,0.0003832293,0.005227631],"genre_scores_gemma":[0.9080303,0.0002400971,0.08907193,0.00005724253,0.00002136131,0.00007515309,0.0000477663,0.00003349874,0.002422664],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002755655,"threshold_uncertainty_score":0.005479217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04780272381947386,"score_gpt":0.3160574489044459,"score_spread":0.2682547250849721,"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."}}