{"id":"W2011902862","doi":"10.1143/jjap.39.2203","title":"Study of Optimization Guidelines on Nitrogen Concentration in Nitrided Oxide for Logic and Dynamic Random Access Memory Application","year":2000,"lang":"en","type":"article","venue":"Japanese Journal of Applied Physics","topic":"Semiconductor materials and devices","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Glaucoma Research Society of Canada","keywords":"Dram; Dynamic random-access memory; Gate oxide; Materials science; Optoelectronics; Degradation (telecommunications); Transistor; Nitriding; Oxide; Logic gate; AND gate; Electronic engineering; Nitrogen; MOSFET; Channel (broadcasting); Computer science; Electrical engineering; Semiconductor memory; Nanotechnology; Computer hardware; Voltage; Chemistry; Engineering; Layer (electronics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002038563,0.00009850187,0.0002558004,0.00003971165,0.00002200106,0.00002762872,0.00009657991,0.00003172588,0.000006382905],"category_scores_gemma":[0.000008738933,0.00008234198,0.00002613412,0.0001238305,0.00001125221,0.0001839558,0.000005208725,0.00004659227,4.698759e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002575203,"about_ca_system_score_gemma":0.00000834584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001026975,"about_ca_topic_score_gemma":0.000003487345,"domain_scores_codex":[0.9992061,0.00001273885,0.000493821,0.0000837157,0.0001213234,0.00008226375],"domain_scores_gemma":[0.9995442,0.00006846761,0.000170337,0.00008223946,0.0001079387,0.00002684033],"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":[0.0004188229,0.00008182706,0.00009799998,0.00002763986,0.00002089663,2.699667e-7,0.0005974419,0.7852135,0.2117346,0.00002553713,0.000003487258,0.001777992],"study_design_scores_gemma":[0.01430179,0.0003135709,0.001326522,0.00006414411,0.0001252939,0.00001076567,0.002910299,0.7119148,0.2624075,0.006275564,0.00001006322,0.0003397432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972112,0.0000402528,0.002076625,0.000006642552,0.00003112641,0.0005268459,0.000002059937,0.00001464704,0.00009063719],"genre_scores_gemma":[0.9988568,0.00005581118,0.0009272089,0.000032343,0.00007145529,0.00003474509,0.000007694067,0.00001343672,5.45042e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07329872,"threshold_uncertainty_score":0.3357808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02614966524914451,"score_gpt":0.2907049401060617,"score_spread":0.2645552748569172,"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."}}