{"id":"W2170918065","doi":"10.1109/tcad.2008.923251","title":"Statistical Thermal Profile Considering Process Variations: Analysis and Applications","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Leakage (economics); Chip; Monte Carlo method; Thermal; Spectrum analyzer; Electronic engineering; Integrated circuit; Hotspot (geology); Process variation; Materials science; Computer science; Reliability engineering; Process (computing); Engineering; Optoelectronics; Electrical engineering; Statistics; Mathematics; Physics","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":[0.0004513244,0.0005516562,0.0003186165,0.000739679,0.0001321086,0.0003695541,0.0004662556,0.0003842675,0.0006904639],"category_scores_gemma":[0.003162015,0.0002416133,0.0003565865,0.001006849,0.0002491252,0.0006885592,0.0002893917,0.0003960987,0.0003292368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003648447,"about_ca_system_score_gemma":0.000318707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001222431,"about_ca_topic_score_gemma":0.0006715658,"domain_scores_codex":[0.9996461,0.00008831044,0.00001409067,0.00005072379,0.0001792457,0.00002154743],"domain_scores_gemma":[0.9988098,0.0006481339,0.000155499,0.0001788414,0.000186425,0.00002122083],"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":[0.00006956791,0.00003356256,0.004413584,0.00007760603,0.00005488055,0.0001481304,0.0000686814,0.8605318,0.03907327,0.01038388,0.000526733,0.08461826],"study_design_scores_gemma":[0.000001341557,0.00001057805,0.001096907,0.000002371906,0.00000448454,0.00005451047,0.000003426559,0.9923655,0.004473321,0.001593461,0.0003882567,0.000005800703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05398341,0.0003648854,0.9424769,0.00005625827,0.00001096805,0.00002883012,0.00008905575,0.001489928,0.001499783],"genre_scores_gemma":[0.943494,0.0004670916,0.05483651,0.00004085209,0.00003033439,0.00006696667,0.0001604571,0.0001735783,0.0007301471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001222431,"threshold_uncertainty_score":0.002647102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0211670936302663,"score_gpt":0.2190209050151152,"score_spread":0.1978538113848489,"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."}}