{"id":"https://openalex.org/W4312121114","doi":"https://doi.org/10.1145/3508352.3549389","title":"Robustify ML-Based Lithography Hotspot Detectors","display_name":"Robustify ML-Based Lithography Hotspot Detectors","publication_year":2022,"publication_date":"2022-10-30","ids":{"openalex":"https://openalex.org/W4312121114","doi":"https://doi.org/10.1145/3508352.3549389"},"language":"en","primary_location":{"id":"doi:10.1145/3508352.3549389","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3508352.3549389","pdf_url":null,"source":{"id":"https://openalex.org/S4363608844","display_name":"Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design","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/A5024467177","display_name":"Jingyu Pan","orcid":"https://orcid.org/0000-0002-7187-5205"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jingyu Pan","raw_affiliation_strings":["Duke University"],"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028018784","display_name":"Chen-Chia Chang","orcid":"https://orcid.org/0000-0003-3115-0733"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen-Chia Chang","raw_affiliation_strings":["Duke University"],"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075696558","display_name":"Zhiyao Xie","orcid":"https://orcid.org/0000-0002-4442-592X"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zhiyao Xie","raw_affiliation_strings":["Hong Kong University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103246390","display_name":"Jiang Hu","orcid":"https://orcid.org/0000-0003-1157-7799"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang Hu","raw_affiliation_strings":["Texas A&M University"],"affiliations":[{"raw_affiliation_string":"Texas A&M University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058073627","display_name":"Yiran Chen","orcid":"https://orcid.org/0000-0002-1486-8412"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiran Chen","raw_affiliation_strings":["Duke University"],"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5024467177"],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":0.8554,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.68172691,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.9995999932289124,"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"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9959999918937683,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6930673122406006},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.677238941192627},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5206881761550903},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5015068054199219},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4978628158569336},{"id":"https://openalex.org/keywords/hotspot","display_name":"Hotspot (geology)","score":0.4857228398323059},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4740928113460541},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4571428894996643},{"id":"https://openalex.org/keywords/lithography","display_name":"Lithography","score":0.4414999186992645},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.43097084760665894},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4212590754032135},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37336206436157227},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.35292932391166687},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11805590987205505},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.10739722847938538}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6930673122406006},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.677238941192627},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5206881761550903},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5015068054199219},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4978628158569336},{"id":"https://openalex.org/C146481406","wikidata":"https://www.wikidata.org/wiki/Q105131","display_name":"Hotspot (geology)","level":2,"score":0.4857228398323059},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4740928113460541},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4571428894996643},{"id":"https://openalex.org/C204223013","wikidata":"https://www.wikidata.org/wiki/Q133036","display_name":"Lithography","level":2,"score":0.4414999186992645},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.43097084760665894},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4212590754032135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37336206436157227},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.35292932391166687},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11805590987205505},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.10739722847938538},{"id":"https://openalex.org/C49040817","wikidata":"https://www.wikidata.org/wiki/Q193091","display_name":"Optoelectronics","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3508352.3549389","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3508352.3549389","pdf_url":null,"source":{"id":"https://openalex.org/S4363608844","display_name":"Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-125828","is_oa":false,"landing_page_url":"http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000981574300133","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1976727896","https://openalex.org/W1995125212","https://openalex.org/W2095410905","https://openalex.org/W2329157012","https://openalex.org/W2794002979","https://openalex.org/W2794271438","https://openalex.org/W2804151869","https://openalex.org/W2888233014","https://openalex.org/W2962949351","https://openalex.org/W2963026800","https://openalex.org/W2974976722","https://openalex.org/W3121891885","https://openalex.org/W3126755924","https://openalex.org/W3210573375","https://openalex.org/W4247200422","https://openalex.org/W4288079630"],"related_works":["https://openalex.org/W2379637199","https://openalex.org/W2405057786","https://openalex.org/W2079602762","https://openalex.org/W2580355466","https://openalex.org/W2765519165","https://openalex.org/W1888682135","https://openalex.org/W2063054109","https://openalex.org/W2294590153","https://openalex.org/W4220659530","https://openalex.org/W3017872546"],"abstract_inverted_index":{"Deep":[0],"learning":[1,21],"has":[2,22],"been":[3],"widely":[4],"applied":[5],"in":[6,26,45,123],"various":[7],"VLSI":[8,48],"design":[9,17,49,98],"automation":[10],"tasks,":[11],"from":[12],"layout":[13],"quality":[14],"estimation":[15],"to":[16,39,57,68,85,109],"optimization.":[18],"Though":[19],"deep":[20,33,71],"shown":[23],"state-of-the-art":[24],"performance":[25],"several":[27,81],"applications,":[28],"recent":[29],"studies":[30],"reveal":[31],"that":[32,135],"neural":[34,72],"networks":[35],"exhibit":[36],"intrinsic":[37],"vulnerability":[38],"adversarial":[40,82],"perturbations,":[41],"which":[42,63,106],"pose":[43],"risks":[44],"the":[46,53,65,70,87,125,128,131,136,143,153,159,165,174,181,184,190,199],"ML-aided":[47],"flow.":[50],"One":[51],"of":[52,89,130,183],"most":[54],"effective":[55],"strategies":[56],"improve":[58,86],"robustness":[59,88,126,145],"is":[60,107],"regularization":[61,103],"approaches,":[62],"adjust":[64],"optimization":[66],"objective":[67],"make":[69],"network":[73],"generalize":[74],"better.":[75],"In":[76],"this":[77],"paper,":[78],"we":[79],"examine":[80],"defense":[83,155,185],"methods":[84],"ML-based":[90,111],"lithography":[91,112],"hotspot":[92,113],"detectors.":[93],"We":[94],"present":[95],"an":[96],"innovative":[97],"rule":[99],"checking":[100],"(DRC)-guided":[101],"curvature":[102],"(CURE)":[104],"approach,":[105],"customized":[108],"robustify":[110],"detectors":[114],"against":[115],"white-box":[116],"attacks.":[117],"Our":[118],"approach":[119],"allows":[120],"for":[121],"improvements":[122],"both":[124],"and":[127,146,172,197],"accuracy":[129,147],"model.":[132],"Experiments":[133],"show":[134],"model":[137],"optimized":[138],"by":[139,170,177,195,202],"DRC-guided":[140,162,187],"CURE":[141,163,188],"achieves":[142],"highest":[144],"compared":[148],"with":[149,158,180],"those":[150],"trained":[151],"using":[152],"baseline":[154],"methods.":[156],"Compared":[157,179],"vanilla":[160],"model,":[161],"decreases":[164],"average":[166,175,191,200],"attack":[167,192],"success":[168,193],"rate":[169,194],"53.9%":[171],"increases":[173],"ROC-AUC":[176,201],"12.1%.":[178],"best":[182],"baselines,":[186],"reduces":[189],"18.6%":[196],"improves":[198],"4.3%.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}