{"id":"W1814593823","doi":"10.1016/j.micpro.2015.09.008","title":"On the reliability estimation of nano-circuits using neural networks","year":2015,"lang":"en","type":"article","venue":"Microprocessors and Microsystems","topic":"Semiconductor materials and devices","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Computer science; Benchmark (surveying); Reliability (semiconductor); Electronic circuit; Artificial neural network; Circuit reliability; Range (aeronautics); Monte Carlo method; Set (abstract data type); Algorithm; Reliability engineering; Artificial intelligence; Mathematics; Statistics","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":[0.0009114497,0.0005852102,0.000764516,0.0008450928,0.0002497371,0.0006045763,0.0008940499,0.000762044,0.0008591167],"category_scores_gemma":[0.004682118,0.0004183695,0.0005512872,0.0005602275,0.0004916087,0.001135969,0.0005073918,0.0007687269,0.0001245652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007740046,"about_ca_system_score_gemma":0.0004664177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007848362,"about_ca_topic_score_gemma":0.005567081,"domain_scores_codex":[0.9996762,0.0001106975,0.00002285022,0.00006817959,0.00008465366,0.00003740229],"domain_scores_gemma":[0.9974791,0.001970656,0.0001608845,0.00007891595,0.0002820569,0.00002841741],"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":[0.00006016337,0.00002244871,0.0008672237,0.00004896161,0.00005124096,0.00002111417,0.00001968185,0.948963,0.001110983,0.002997577,0.0002224613,0.04561519],"study_design_scores_gemma":[9.232142e-7,0.000004265855,0.0001266428,0.000002313906,0.000003609786,0.000002368164,0.000001094522,0.9986699,0.0001779116,0.0009792588,0.00003035366,0.000001449304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08139334,0.002029208,0.9138281,0.000397993,0.00005379623,0.00003660225,0.00007004798,0.0003304381,0.001860492],"genre_scores_gemma":[0.945403,0.0008611493,0.05148041,0.00009713407,0.00008808846,0.00005678086,0.0001194908,0.00004007297,0.001853936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007848362,"threshold_uncertainty_score":0.01560533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02369222629737918,"score_gpt":0.2273711010949222,"score_spread":0.203678874797543,"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."}}