{"id":"W4400237890","doi":"10.1109/iscas58744.2024.10558430","title":"A Rule-Based High Efficient Obstacle-Avoiding RSMT Algorithm for VLSI Routing","year":2024,"lang":"en","type":"article","venue":"","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"National Natural Science Foundation of China","keywords":"Very-large-scale integration; Obstacle; Computer science; Routing (electronic design automation); Parallel computing; Routing algorithm; Algorithm design; Algorithm; Embedded system; Routing protocol","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":[0.0008388371,0.0001470713,0.0001652317,0.0001301132,0.000274256,0.0007180916,0.0003755824,0.00006780418,0.00002647417],"category_scores_gemma":[0.00003002133,0.000117927,0.0001602523,0.0003864897,0.00001310952,0.0001545775,0.00008106641,0.000144054,0.00007366983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001128558,"about_ca_system_score_gemma":0.00006935577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009079505,"about_ca_topic_score_gemma":0.000005909231,"domain_scores_codex":[0.9985579,0.00006590766,0.0003078886,0.0004798257,0.0002141087,0.0003743402],"domain_scores_gemma":[0.9989889,0.0004987759,0.00004299167,0.0002974853,0.00009474208,0.00007707289],"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":[0.000005812301,0.00008885768,0.00001930109,0.0001380236,0.00008122918,0.00006226763,0.001167097,0.04463379,0.0007020248,0.3851568,0.01325082,0.554694],"study_design_scores_gemma":[0.0002201404,0.00006055005,0.000009455453,0.000139799,0.000005234006,0.00001697351,0.0000884059,0.9900354,0.002439733,0.0004665712,0.006346771,0.0001709865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004489867,0.0001634673,0.9875007,0.0006305975,0.004778766,0.0002858153,0.000004589826,0.0006946838,0.001451544],"genre_scores_gemma":[0.9196208,5.081337e-7,0.07787926,0.0003192151,0.0004943602,0.00005926565,0.000003446087,0.00001700804,0.001606201],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9454016,"threshold_uncertainty_score":0.6924574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611258267738973,"score_gpt":0.2460645267439518,"score_spread":0.229951944066562,"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."}}