{"id":"W2015710739","doi":"10.1109/tvlsi.2011.2107583","title":"On-Chip Process Variations Compensation Using an Analog Adaptive Body Bias (A-ABB)","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Compensation (psychology); Process (computing); Chip; Computer science; Electronic engineering; Analogue electronics; Process variation; Electronic circuit; Electrical engineering; Engineering; Telecommunications; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000574753,0.0005142061,0.000474446,0.0007745738,0.0005379383,0.0001742782,0.0003103882,0.0003371065,0.0001944124],"category_scores_gemma":[0.00001199818,0.0004966377,0.0001727576,0.0008820844,0.00006342904,0.001319731,0.000001278467,0.0006222444,0.0002603776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000510278,"about_ca_system_score_gemma":0.0001112754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003082378,"about_ca_topic_score_gemma":0.0006158896,"domain_scores_codex":[0.9972061,0.0002796226,0.0008374015,0.0005448341,0.0005971627,0.0005348881],"domain_scores_gemma":[0.9984549,0.0001365319,0.0001915561,0.0006310832,0.0003644133,0.0002215286],"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":[0.000247294,0.001264909,0.0001439892,0.0001606821,0.0003318888,0.00001279791,0.01078181,0.9686545,0.01365664,0.003110018,0.0001608888,0.001474616],"study_design_scores_gemma":[0.0007803079,0.0004620527,0.0004560067,0.0003057437,0.0001331873,0.00003105437,0.001916643,0.947956,0.04713552,0.0001670845,0.00005890593,0.0005974922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2563546,0.0000233759,0.7373407,0.000006291753,0.002684072,0.0007709768,0.0002099042,0.0006499633,0.001960104],"genre_scores_gemma":[0.9978322,0.00001410102,0.001315143,0.00004477562,0.0001782824,0.0002536016,0.00007055517,0.0001169201,0.0001744675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7414776,"threshold_uncertainty_score":0.9997485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05358618500473177,"score_gpt":0.2493811235303203,"score_spread":0.1957949385255885,"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."}}