{"id":"W4289782747","doi":"","title":"3D Digital SiPM and Smart Silicon Interposer for nEXO","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"3D IC and TSV technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Silicon photomultiplier; Silicon; Optoelectronics; Interposer; Computer science; Materials science; Detector; Telecommunications; Nanotechnology; Etching (microfabrication); Layer (electronics)","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":[0.0001151174,0.0002969135,0.0002407726,0.0001784121,0.000283058,0.0006574907,0.0004108983,0.0006341901,0.005354728],"category_scores_gemma":[0.0001540744,0.000154945,0.0002280348,0.0001386928,0.0002601356,0.0005085056,0.0006049232,0.0003256748,0.001095804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004484041,"about_ca_system_score_gemma":0.0003734206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004724775,"about_ca_topic_score_gemma":0.001524606,"domain_scores_codex":[0.9999046,0.00001029566,0.000002822123,0.00001902581,0.00004641312,0.00001686485],"domain_scores_gemma":[0.9999332,0.00000964955,0.000006708724,0.00002256195,0.0000162155,0.00001170684],"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":[0.0005996979,0.0001851705,0.002127001,0.0003811025,0.0000657011,0.001250289,0.0003217641,0.09424663,0.6739073,0.1006348,0.01794182,0.1083388],"study_design_scores_gemma":[0.00006659147,0.0005220789,0.002867923,0.0001067497,0.00005523292,0.001181281,0.0002594265,0.4513917,0.3621352,0.02461603,0.1566912,0.0001066006],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5377897,0.002260798,0.2465619,0.001587574,0.0009197636,0.0001155158,0.001201977,0.004887369,0.2046753],"genre_scores_gemma":[0.9063153,0.0005321997,0.06792951,0.0002283601,0.00005043797,0.00005814074,0.000428621,0.0002185266,0.02423891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005354728,"threshold_uncertainty_score":0.01791334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008732218100106,"score_gpt":0.2024333131863392,"score_spread":0.1923459910053382,"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."}}