{"id":"W1978718503","doi":"10.1109/tim.2014.2313431","title":"On System-on-Chip Testing Using Hybrid Test Vector Compression","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lossless compression; Computer science; Test vector; Embedded system; System on a chip; Computer hardware; Test compression; Overhead (engineering); Benchmark (surveying); Data compression; Very-large-scale integration; Automatic test pattern generation; Integration testing; Fault coverage; Electronic circuit; Software; Engineering; Test set; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004202428,0.0005459218,0.0003367768,0.0009283786,0.0002026961,0.0004050613,0.0006883398,0.0003684191,0.001435035],"category_scores_gemma":[0.001673489,0.0001331703,0.0002590029,0.0008657289,0.0004517654,0.000936886,0.0005043352,0.0002948444,0.0002789632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003350018,"about_ca_system_score_gemma":0.0003367089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005822714,"about_ca_topic_score_gemma":0.0007901814,"domain_scores_codex":[0.9990242,0.0002203897,0.0000400223,0.00009330051,0.0005691081,0.00005294494],"domain_scores_gemma":[0.998855,0.0005596704,0.0001016417,0.0002528013,0.0002064451,0.00002438135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004766747,0.0001494191,0.002912153,0.0002088635,0.00008543137,0.0002106468,0.0001227696,0.0964753,0.1189997,0.01066751,0.00119861,0.7684929],"study_design_scores_gemma":[0.00005480066,0.0009861996,0.00296002,0.00003844402,0.00005949145,0.0009649452,0.00004097386,0.8218744,0.1620846,0.005298861,0.005609045,0.0000282321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1056248,0.000711309,0.8873325,0.00009994562,0.00002886158,0.0001421868,0.00005923819,0.001531494,0.004469685],"genre_scores_gemma":[0.7874305,0.0002875612,0.2094614,0.00008126679,0.00003586641,0.0001224051,0.0001916506,0.00008841909,0.002300966],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001435035,"threshold_uncertainty_score":0.004800677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06817084494368852,"score_gpt":0.2532771614171576,"score_spread":0.1851063164734691,"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."}}