{"id":"W2123442619","doi":"10.1002/pssa.201532033","title":"Camera‐based high frequency heterodyne lock‐in carrierographic (frequency‐domain photoluminescence) imaging of crystalline silicon wafers","year":2015,"lang":"en","type":"article","venue":"physica status solidi (a)","topic":"Integrated Circuits and Semiconductor Failure Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Recruitment Program of Global Experts; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Heterodyne (poetry); Optics; Materials science; Wafer; Amplitude; Frequency modulation; SIGNAL (programming language); Heterodyne detection; Amplitude modulation; Physics; Optoelectronics; Radio frequency; Acoustics; Computer science; Telecommunications; Laser","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":[0.0002062116,0.0004677426,0.0007618269,0.0004652198,0.00005277606,0.0000516439,0.0003436362,0.0001093966,0.00005462399],"category_scores_gemma":[0.0000436978,0.0004670928,0.000268754,0.001255057,0.0002058883,0.0003401819,0.00002093506,0.0004285715,0.00001216571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003030831,"about_ca_system_score_gemma":0.000187861,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006923195,"about_ca_topic_score_gemma":0.001023164,"domain_scores_codex":[0.9973803,0.0000961012,0.0006740578,0.0004774648,0.00046554,0.0009064971],"domain_scores_gemma":[0.9985284,0.00005929276,0.0001474958,0.0006315079,0.0003021291,0.0003311967],"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":[0.00002125703,0.0001814711,0.0241464,0.0001416472,0.0001955137,0.00006416449,0.001649755,0.0104993,0.961107,0.00123377,0.0004504069,0.0003092814],"study_design_scores_gemma":[0.01318989,0.0008114234,0.01197136,0.001289189,0.001104571,0.00003713686,0.01429093,0.2200319,0.7045514,0.02471805,0.001521949,0.006482254],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956216,0.0008108254,0.0008336573,0.00009585376,0.0002343522,0.0002577538,0.0001564356,0.0002115767,0.001777895],"genre_scores_gemma":[0.9985759,0.00005488782,0.0008537809,0.00006402333,0.0001105483,0.00004680752,0.0001933967,0.00009017356,0.00001052361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2565556,"threshold_uncertainty_score":0.9997781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01111260869976406,"score_gpt":0.2183874362724811,"score_spread":0.207274827572717,"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."}}