{"id":"https://openalex.org/W4414197815","doi":"https://doi.org/10.1109/dac63849.2025.11133380","title":"Delving into Topology Representation for Layout Pattern: A Novel Contrastive Learning Framework for Hotspot Detection","display_name":"Delving into Topology Representation for Layout Pattern: A Novel Contrastive Learning Framework for Hotspot Detection","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4414197815","doi":"https://doi.org/10.1109/dac63849.2025.11133380"},"language":"en","primary_location":{"id":"doi:10.1109/dac63849.2025.11133380","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac63849.2025.11133380","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 62nd ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016267767","display_name":"Silin Chen","orcid":"https://orcid.org/0009-0000-5086-2184"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Silin Chen","raw_affiliation_strings":["Nanjing University,School of Integrated Circuits,Suzhou,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University,School of Integrated Circuits,Suzhou,China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083755549","display_name":"Kangjian Di","orcid":null},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kangjian Di","raw_affiliation_strings":["Nanjing University,School of Integrated Circuits,Suzhou,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University,School of Integrated Circuits,Suzhou,China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101566931","display_name":"Guohao Wang","orcid":"https://orcid.org/0000-0003-0922-7490"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guohao Wang","raw_affiliation_strings":["ZetaTech Co.,Ltd"],"affiliations":[{"raw_affiliation_string":"ZetaTech Co.,Ltd","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114488826","display_name":"W. Z. Zhao","orcid":"https://orcid.org/0009-0005-9575-9232"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenzheng Zhao","raw_affiliation_strings":["ZetaTech Co.,Ltd"],"affiliations":[{"raw_affiliation_string":"ZetaTech Co.,Ltd","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104294805","display_name":"Li Du","orcid":"https://orcid.org/0009-0003-6284-4612"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Du","raw_affiliation_strings":["Nanjing University,School of Electronic Science and Engineering,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University,School of Electronic Science and Engineering,Nanjing,China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035530794","display_name":"Ningmu Zou","orcid":"https://orcid.org/0000-0003-2505-4139"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ningmu Zou","raw_affiliation_strings":["Nanjing University,School of Integrated Circuits,Suzhou,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University,School of Integrated Circuits,Suzhou,China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5016267767"],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3398581,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.8457000255584717,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.8457000255584717,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4869000017642975},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.48559999465942383},{"id":"https://openalex.org/keywords/polygon","display_name":"Polygon (computer graphics)","score":0.4672999978065491},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.42089998722076416},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.41589999198913574},{"id":"https://openalex.org/keywords/hotspot","display_name":"Hotspot (geology)","score":0.4154999852180481},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.40389999747276306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6854000091552734},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.48559999465942383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4828999936580658},{"id":"https://openalex.org/C190694206","wikidata":"https://www.wikidata.org/wiki/Q3276654","display_name":"Polygon (computer graphics)","level":3,"score":0.4672999978065491},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.42089998722076416},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.41589999198913574},{"id":"https://openalex.org/C146481406","wikidata":"https://www.wikidata.org/wiki/Q105131","display_name":"Hotspot (geology)","level":2,"score":0.4154999852180481},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3772999942302704},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33970001339912415},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3138999938964844},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3003000020980835},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.2824999988079071},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27639999985694885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2736000120639801},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2669000029563904},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dac63849.2025.11133380","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac63849.2025.11133380","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 62nd ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2100262960","https://openalex.org/W2159498975","https://openalex.org/W2194775991","https://openalex.org/W2533275277","https://openalex.org/W2920810201","https://openalex.org/W2945421679","https://openalex.org/W2963163009","https://openalex.org/W2998417722","https://openalex.org/W3048563620","https://openalex.org/W3138516171","https://openalex.org/W3199630607","https://openalex.org/W3214178105","https://openalex.org/W4213282001","https://openalex.org/W4280510627","https://openalex.org/W4379115969","https://openalex.org/W4386763651","https://openalex.org/W4399487326"],"related_works":[],"abstract_inverted_index":{"Recently,":[0],"machine":[1],"learning-based":[2],"techniques":[3],"have":[4],"been":[5],"applied":[6],"for":[7,51],"layout":[8,47],"hotspot":[9,52],"detection.":[10,53],"However,":[11],"existing":[12],"methods":[13],"encounter":[14],"challenges":[15],"in":[16,120,149],"capturing":[17],"the":[18,22,27,33,57,80,100,115],"decision":[19,72],"boundary":[20],"across":[21],"entire":[23],"dataset":[24],"and":[25,30,49,63,146],"ignore":[26],"geometric":[28,77],"properties":[29],"topology":[31,106],"of":[32,69],"polygons.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38,84,108],"introduce":[39],"CLI-HD,":[40],"a":[41,70,147],"novel":[42],"contrastive":[43],"learning":[44],"framework":[45,55],"on":[46,125],"sequences":[48,94],"images":[50],"Our":[54],"improves":[56],"ability":[58],"to":[59,103,143,157],"distinguish":[60],"between":[61],"hotspots":[62],"non-hotspots":[64],"by":[65,153],"similarity":[66],"computations":[67],"instead":[68],"single":[71],"boundary.":[73],"To":[74],"effectively":[75],"incorporate":[76],"information":[78],"into":[79,99],"model":[81],"training":[82],"process,":[83],"propose":[85],"Layout2Seq,":[86],"which":[87],"encodes":[88],"polygon":[89],"shapes":[90],"as":[91],"vectors":[92],"within":[93],"that":[95,129],"are":[96],"subsequently":[97],"fed":[98],"CLIHD.":[101],"Furthermore,":[102],"better":[104],"represent":[105],"information,":[107],"develop":[109],"an":[110,136],"absolute":[111],"position":[112,117],"embedding,":[113],"replacing":[114],"standard":[116],"encoders":[118],"used":[119],"Transformer":[121],"architectures.":[122],"Extensive":[123],"evaluations":[124],"various":[126],"benchmarks":[127],"demonstrate":[128],"CLI-HD":[130],"outperforms":[131],"current":[132],"state-of-the-art":[133],"methods,":[134],"with":[135],"accuracy":[137],"improvement":[138],"ranging":[139],"from":[140],"$0.82":[141],"\\%$":[142,145],"$4.77":[144],"reduction":[148],"false":[150],"alarm":[151],"rates":[152],"$\\mathbf{4.":[154],"9":[155],"\\%}$":[156],"$\\mathbf{2":[158],"3.":[159],"1":[160],"8":[161],"\\%}$.":[162]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}