{"id":"W2581360948","doi":"10.31399/asm.cp.istfa2016p0480","title":"Elemental Characterization of Sub 20 nm Structures in Devices Using Low Energy SEM-EDS","year":2016,"lang":"en","type":"article","venue":"Proceedings - International Symposium for Testing and Failure Analysis","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Oxford Instruments (Canada)","funders":"","keywords":"Detector; Scanning electron microscope; Characterization (materials science); Materials science; Resolution (logic); Scanning transmission electron microscopy; Electronics; Energy (signal processing); Optoelectronics; Transmission electron microscopy; Semiconductor; X-ray detector; Elemental analysis; Nanotechnology; Optics; Computer science; Electrical engineering; Physics; Engineering; Chemistry; Composite material; Artificial intelligence","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":[0.0001572412,0.000291285,0.0002273302,0.0004449469,0.0002197968,0.0004902462,0.0005042595,0.0003917154,0.001750731],"category_scores_gemma":[0.0002166112,0.0001873789,0.0001231198,0.0001937486,0.0002452876,0.0005105403,0.0002258957,0.0002493709,0.0004416615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00028456,"about_ca_system_score_gemma":0.0001461353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004763124,"about_ca_topic_score_gemma":0.001377954,"domain_scores_codex":[0.9998877,0.000004544081,0.00000739062,0.00003097773,0.00005518155,0.00001413125],"domain_scores_gemma":[0.9998197,0.00005055004,0.00003289617,0.0000283993,0.00005723849,0.00001123087],"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":[0.0000245614,0.00001661285,0.0008673684,0.00004676104,0.000005587165,0.00007617374,0.00004529397,0.0002247203,0.9948395,0.0002053412,0.00008132654,0.003566877],"study_design_scores_gemma":[0.000004174513,0.0001049599,0.009554778,0.000009642486,0.00001564463,0.0001789509,0.0000806532,0.002746104,0.9841623,0.0001496966,0.00298611,0.000006914418],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.958963,0.001026733,0.03327186,0.00008095285,0.00005772318,0.00006628264,0.0007779609,0.000485477,0.00527004],"genre_scores_gemma":[0.9455105,0.0006525717,0.04727422,0.00006725895,0.000008227355,0.00006119414,0.0005673668,0.000144925,0.005713656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001750731,"threshold_uncertainty_score":0.005856812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009248835036303448,"score_gpt":0.2562916988970083,"score_spread":0.2470428638607049,"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."}}