{"id":"W4240260309","doi":"10.26434/chemrxiv.13129910.v3","title":"Resolving impurities in atomic layer deposited aluminum nitride through low cost, high efficiency precursor design","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Semiconductor materials and devices","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Linköpings Universitet; Stiftelsen för Strategisk Forskning","keywords":"Impurity; Atomic layer deposition; Nitride; Materials science; Layer (electronics); Aluminium; Deposition (geology); Thin film; Characterization (materials science); Chemical engineering; Nanotechnology; Metallurgy; Chemistry; Organic chemistry; Engineering","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":[0.0002816372,0.000399353,0.000238095,0.0002372151,0.0002658389,0.0006041943,0.000505189,0.0003855767,0.00064256],"category_scores_gemma":[0.000506961,0.0002263513,0.0001160536,0.0001687679,0.0002147511,0.0005816341,0.000354565,0.0003601953,0.00038899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004177076,"about_ca_system_score_gemma":0.0003947306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004947001,"about_ca_topic_score_gemma":0.001406305,"domain_scores_codex":[0.9997739,0.00003082824,0.00001453779,0.00005579958,0.0001012188,0.00002374537],"domain_scores_gemma":[0.9998205,0.00004324471,0.00005114629,0.00003163422,0.00004118921,0.00001226419],"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":[0.00007961555,0.00001596168,0.0002961839,0.0001126925,0.00000693214,0.00009154347,0.00003900684,0.0004723528,0.9908488,0.0007943066,0.0000843552,0.007158256],"study_design_scores_gemma":[0.000009956012,0.00006889738,0.0002471865,0.000003224617,0.00000592471,0.0001234495,0.00001327188,0.002075489,0.995469,0.0001178386,0.001861147,0.000004686156],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9217821,0.002526916,0.06696619,0.0001828729,0.00006051896,0.0002310016,0.0002845697,0.0005468838,0.007419023],"genre_scores_gemma":[0.8731588,0.001530146,0.121337,0.00008049869,0.00001857392,0.0001485641,0.0003382238,0.0001721035,0.003216072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00064256,"threshold_uncertainty_score":0.003030717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03867436090198008,"score_gpt":0.2477247183819567,"score_spread":0.2090503574799766,"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."}}