{"id":"W3211697685","doi":"10.1109/tcpmt.2021.3126284","title":"Modeling and Analysis of Silver-Sintered Molybdenum Packaging for SiC Power Modules With Improved Lifetime and Temperature Range","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Components Packaging and Manufacturing Technology","topic":"Silicon Carbide Semiconductor Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hamilton Health Sciences; Natural Resources Canada; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Sintering; Silicon carbide; Molybdenum; Atmospheric temperature range; Electronic packaging; Power module; Die (integrated circuit); Composite material; Optoelectronics; Power (physics); Metallurgy; Nanotechnology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00007434719,0.0003177544,0.0005610204,0.0009780511,0.0001439034,0.00005636098,0.0001271772,0.0002766538,0.000004034018],"category_scores_gemma":[0.000008398777,0.000307042,0.00008460713,0.0003254015,0.0001823334,0.0001292076,0.00001032772,0.0004212014,2.095405e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004172955,"about_ca_system_score_gemma":0.000008540157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000194411,"about_ca_topic_score_gemma":0.00003767541,"domain_scores_codex":[0.9988015,0.00001605409,0.0002566342,0.0004912055,0.00009934396,0.0003352893],"domain_scores_gemma":[0.9993203,0.00008883341,0.00004908818,0.0004210293,0.00005721245,0.00006353726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005433467,0.00004098796,0.000387581,0.0001658627,0.001365534,0.00001131528,0.0002343172,0.03545917,0.9528652,0.0000245915,0.000004928172,0.009386195],"study_design_scores_gemma":[0.0008533163,0.00007215691,0.0006047047,0.0001259218,0.0004766955,0.00005435587,0.000667098,0.196682,0.7999862,0.000166971,0.00001083362,0.0002997217],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9514666,0.0008731691,0.04616344,0.0002973524,0.0001286662,0.0002158199,0.00008653272,0.0007623198,0.000006080624],"genre_scores_gemma":[0.9972881,0.0002939292,0.002265935,0.00002381881,0.000006618519,0.00004324355,0.000009110716,0.00005173631,0.00001751791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1612229,"threshold_uncertainty_score":0.9999382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00996854852550868,"score_gpt":0.210304909171585,"score_spread":0.2003363606460763,"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."}}