{"id":"W1899813288","doi":"10.1109/iscas.2004.1329462","title":"A placement algorithm for implementation of analog LSI/VLSI systems","year":2004,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Simulated annealing; Nondeterministic algorithm; Computer science; Very-large-scale integration; Algorithm; Analogue electronics; Placement; Adder; Floorplan; Electronic circuit; Mathematical optimization; Physical design; Circuit design; Mathematics; Engineering; Embedded system","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":[0.0002062295,0.0005880077,0.0002810476,0.0003513689,0.0003901344,0.0004766083,0.0005428813,0.000426043,0.00509812],"category_scores_gemma":[0.0004113086,0.0002099776,0.0002676736,0.0004144733,0.0002304364,0.0003588868,0.0002768446,0.0004527282,0.001128755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004865408,"about_ca_system_score_gemma":0.0005184929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001060247,"about_ca_topic_score_gemma":0.001574671,"domain_scores_codex":[0.9998933,0.00002439404,0.000008405947,0.00002146968,0.0000399644,0.00001245039],"domain_scores_gemma":[0.9999214,0.00002289531,0.00001145373,0.00001344758,0.00002625243,0.00000452209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007562277,0.00005414772,0.0002411138,0.000222736,0.00003224384,0.0001198641,0.00008373161,0.3780342,0.0304176,0.04883161,0.004581612,0.5373055],"study_design_scores_gemma":[0.00002947794,0.0001162429,0.0001794091,0.00002219821,0.0000172961,0.0001279028,0.00002161604,0.9619008,0.01069755,0.01246897,0.0144097,0.000008920802],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004461878,0.0001332394,0.9910466,0.00006992814,0.00003300016,0.00006137056,0.00003848078,0.0006895259,0.003465921],"genre_scores_gemma":[0.1174617,0.0001837132,0.8777766,0.00005062633,0.00002090704,0.0001613388,0.0001143398,0.00007908441,0.004151597],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00509812,"threshold_uncertainty_score":0.01705498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01189656039472425,"score_gpt":0.2670548869184798,"score_spread":0.2551583265237555,"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."}}