{"id":"https://openalex.org/W4318256970","doi":"https://doi.org/10.1145/3557988.3569710","title":"Neural Network Model for Detour Net Prediction","display_name":"Neural Network Model for Detour Net Prediction","publication_year":2022,"publication_date":"2022-11-03","ids":{"openalex":"https://openalex.org/W4318256970","doi":"https://doi.org/10.1145/3557988.3569710"},"language":"en","primary_location":{"id":"doi:10.1145/3557988.3569710","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3557988.3569710","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3557988.3569710","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM/IEEE Workshop on System Level Interconnect Pathfinding","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3557988.3569710","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089499900","display_name":"Jaehoon Ahn","orcid":"https://orcid.org/0000-0002-7285-8626"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaehoon Ahn","raw_affiliation_strings":["Seoul National University, Gwanak-gu, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seoul National University, Gwanak-gu, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049474875","display_name":"Taewhan Kim","orcid":"https://orcid.org/0000-0002-6114-3772"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Taewhan Kim","raw_affiliation_strings":["Seoul National University, Gwanak-gu, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seoul National University, Gwanak-gu, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":0.0923,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43535771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12371","display_name":"Electrical Contact Performance and Analysis","score":0.9642999768257141,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9550999999046326,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7779948711395264},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6576178669929504},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.5936601161956787},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.5913608074188232},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5722234845161438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49428510665893555},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46146515011787415},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4470519423484802},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3856459856033325},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08923149108886719}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7779948711395264},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6576178669929504},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.5936601161956787},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.5913608074188232},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5722234845161438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49428510665893555},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46146515011787415},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4470519423484802},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3856459856033325},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08923149108886719},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3557988.3569710","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3557988.3569710","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3557988.3569710","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM/IEEE Workshop on System Level Interconnect Pathfinding","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3557988.3569710","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3557988.3569710","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3557988.3569710","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM/IEEE Workshop on System Level Interconnect Pathfinding","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2099184857","https://openalex.org/W2946795101","https://openalex.org/W3012124806","https://openalex.org/W3012412994","https://openalex.org/W3109998468","https://openalex.org/W3211934115","https://openalex.org/W4312076509","https://openalex.org/W6687483927"],"related_works":["https://openalex.org/W2912321008","https://openalex.org/W1998607122","https://openalex.org/W2324368075","https://openalex.org/W2972124131","https://openalex.org/W338149487","https://openalex.org/W4403012196","https://openalex.org/W2972032537","https://openalex.org/W4318612353","https://openalex.org/W4389829534","https://openalex.org/W4390939596"],"abstract_inverted_index":{"Identifying":[0],"nets":[1,38,68,83,143],"in":[2,14,19,22,53,75,144,167,198],"a":[3,43,54,99,219],"placement":[4,80],"which":[5],"will":[6],"be":[7,11,40],"very":[8,17],"likely":[9],"to":[10,151,218,229],"detoured":[12],"routes":[13],"routing":[15,26,73],"is":[16,184,227],"useful":[18],"that":[20,186,201],"(1)":[21],"conjunction":[23],"with":[24,200],"the":[25,48,65,76,124,134,139,145,152,161,173,191,204,224,231,237,244],"congestion,":[27],"path":[28],"timing,":[29],"or":[30,131],"design":[31],"rule":[32],"violation":[33,248],"(DRV)":[34],"prediction,":[35],"predicting":[36],"detour":[37,66,103,209],"can":[39,60],"used":[41],"as":[42,88,90],"complementary":[44,174],"means":[45],"of":[46,50,79,111,141,147,163,169,176,223,246],"characterizing":[47],"outcome":[49],"those":[51,82],"predictions":[52],"more":[55,62,85],"depth":[56],"and":[57,72,116,159,172,179,243],"(2)":[58],"we":[59,157],"place":[61],"importance":[63],"on":[64,196,252],"predicted":[67],"for":[69],"optimizing":[70],"timing":[71,86,177,221,247],"resources":[74],"early":[77],"stage":[78],"since":[81],"consume":[84],"budget":[87],"well":[89],"metal/via":[91],"resources.":[92],"In":[93],"this":[94,96],"context,":[95],"work":[97],"proposes":[98],"neural":[100],"network":[101],"based":[102,115,121,136],"net":[104,210],"prediction":[105,165,178,192,211,216],"model.":[106,212],"Our":[107],"proposed":[108,188],"model":[109,122,137,166,189,217],"consists":[110],"two":[112],"parts:":[113],"CNN":[114,120,153],"ANN":[117,135,207],"based.":[118],"The":[119],"processes":[123,138],"features":[125,140],"describing":[126],"various":[127],"physical":[128],"proximity":[129],"maps":[130],"states":[132],"while":[133],"individual":[142],"form":[146],"vector":[148],"descriptions,":[149],"concatenated":[150],"outputs.":[154],"Through":[155],"experiments,":[156],"analyze":[158],"assess":[160],"accuracy":[162,193],"our":[164,187,215],"terms":[168],"F1":[170],"score":[171],"role":[175],"optimization.":[180],"More":[181],"specifically,":[182],"it":[183],"shown":[185],"improves":[190],"by":[194,203,235,241,250],"9.9%":[195],"average":[197],"comparison":[199],"produced":[202],"conventional":[205],"(vanilla":[206],"based)":[208],"Furthermore,":[213],"linking":[214],"state-of-the-art":[220],"optimization":[222],"commercial":[225],"tool":[226],"able":[228],"reduce":[230],"worst":[232],"negative":[233,239],"slack":[234,240],"18.4%,":[236],"total":[238],"40.8%,":[242],"number":[245],"paths":[249],"30.9%":[251],"average.":[253]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
