{"id":"W2007777346","doi":"10.1017/s1431927610057284","title":"Silicon Chip Teardown to the Atomic Scale – Challenges Facing the Reverse Engineering of Semiconductors","year":2010,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Chipworks (Canada)","funders":"","keywords":"Silicon; Silicon chip; Semiconductor; Materials science; Atomic units; Nanotechnology; Reverse engineering; Chip; Scale (ratio); Optoelectronics; Engineering physics; Engineering; Electrical engineering; Computer science; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008048683,0.0002147905,0.0003319476,0.0001274481,0.000242525,0.00009212701,0.0004831023,0.00009754361,0.0001516804],"category_scores_gemma":[0.0000637663,0.0001298935,0.0001302218,0.00027012,0.0001602814,0.00009958359,0.0001380184,0.0003226807,0.00002837278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002560761,"about_ca_system_score_gemma":0.00003249029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003660076,"about_ca_topic_score_gemma":0.0007891865,"domain_scores_codex":[0.9987403,0.0000620312,0.0002971282,0.0003836158,0.0001397532,0.0003771598],"domain_scores_gemma":[0.9990524,0.00008392036,0.0001162254,0.0005994479,0.00006752471,0.00008046507],"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":[0.0000132312,0.00001776102,0.000299273,0.0000217351,0.00002894977,3.918767e-7,0.001361563,0.00001003743,0.9970367,0.0001756231,0.0006241453,0.0004105793],"study_design_scores_gemma":[0.00008319323,0.00004246032,0.0005280729,0.00002503971,0.0001021205,0.0000119739,0.0004062948,0.00004931581,0.9884232,0.00004429542,0.01012608,0.0001579968],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956605,0.001518472,0.0002426399,0.001967981,0.0001572222,0.0002362312,0.00002385755,0.0000788475,0.0001142096],"genre_scores_gemma":[0.9958837,0.0005172504,0.002893392,0.0003354874,0.00008458341,0.00002430272,0.00000280626,0.00002154105,0.000236939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009501939,"threshold_uncertainty_score":0.5296901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007873757717438994,"score_gpt":0.2471773867451336,"score_spread":0.2393036290276946,"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."}}