{"id":"https://openalex.org/W4297822291","doi":"https://doi.org/10.1145/3551901.3556479","title":"Autoencoder-Based Data Sampling for Machine Learning-Based Lithography Hotspot Detection","display_name":"Autoencoder-Based Data Sampling for Machine Learning-Based Lithography Hotspot Detection","publication_year":2022,"publication_date":"2022-09-06","ids":{"openalex":"https://openalex.org/W4297822291","doi":"https://doi.org/10.1145/3551901.3556479"},"language":"en","primary_location":{"id":"doi:10.1145/3551901.3556479","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3551901.3556479","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD","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/A5061995120","display_name":"Mohamed Tarek Ismail","orcid":null},"institutions":[{"id":"https://openalex.org/I80693520","display_name":"American University in Cairo","ror":"https://ror.org/0176yqn58","country_code":"EG","type":"education","lineage":["https://openalex.org/I80693520"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Mohamed Tarek Ismail","raw_affiliation_strings":["Siemens EDA & American University in Cairo, Cairo, Egypt"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens EDA & American University in Cairo, Cairo, Egypt","institution_ids":["https://openalex.org/I80693520"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045449467","display_name":"Hossam Sharara","orcid":"https://orcid.org/0000-0003-0042-9790"},"institutions":[{"id":"https://openalex.org/I80693520","display_name":"American University in Cairo","ror":"https://ror.org/0176yqn58","country_code":"EG","type":"education","lineage":["https://openalex.org/I80693520"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Hossam Sharara","raw_affiliation_strings":["American University in Cairo, Cairo, Egypt"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"American University in Cairo, Cairo, Egypt","institution_ids":["https://openalex.org/I80693520"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061563066","display_name":"Kareem Madkour","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kareem Madkour","raw_affiliation_strings":["Siemens EDA, Cairo, Egypt"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens EDA, Cairo, Egypt","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055400388","display_name":"Karim G. Seddik","orcid":"https://orcid.org/0000-0002-2279-592X"},"institutions":[{"id":"https://openalex.org/I80693520","display_name":"American University in Cairo","ror":"https://ror.org/0176yqn58","country_code":"EG","type":"education","lineage":["https://openalex.org/I80693520"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Karim Seddik","raw_affiliation_strings":["American University in Cairo, Cairo, Egypt"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"American University in Cairo, Cairo, Egypt","institution_ids":["https://openalex.org/I80693520"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08052309,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"91","last_page":"96"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11338","display_name":"Advancements in Photolithography Techniques","score":0.9998999834060669,"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/T11338","display_name":"Advancements in Photolithography Techniques","score":0.9998999834060669,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9973000288009644,"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"}},{"id":"https://openalex.org/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9933000206947327,"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":0.7391564846038818},{"id":"https://openalex.org/keywords/hotspot","display_name":"Hotspot (geology)","score":0.6537415385246277},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6205742359161377},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5827869176864624},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5347698330879211},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5223199725151062},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47430089116096497},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4627302885055542},{"id":"https://openalex.org/keywords/design-for-manufacturability","display_name":"Design for manufacturability","score":0.455110639333725},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44834253191947937}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7391564846038818},{"id":"https://openalex.org/C146481406","wikidata":"https://www.wikidata.org/wiki/Q105131","display_name":"Hotspot (geology)","level":2,"score":0.6537415385246277},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6205742359161377},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5827869176864624},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5347698330879211},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5223199725151062},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47430089116096497},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4627302885055542},{"id":"https://openalex.org/C62064638","wikidata":"https://www.wikidata.org/wiki/Q553878","display_name":"Design for manufacturability","level":2,"score":0.455110639333725},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44834253191947937},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3551901.3556479","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3551901.3556479","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1673310716","https://openalex.org/W2032647444","https://openalex.org/W2083951140","https://openalex.org/W2302532728","https://openalex.org/W2531057735","https://openalex.org/W2553320496","https://openalex.org/W2914304175","https://openalex.org/W2922653022","https://openalex.org/W2923442721","https://openalex.org/W3119492148","https://openalex.org/W3129478262","https://openalex.org/W6922051196"],"related_works":["https://openalex.org/W2094969048","https://openalex.org/W994558755","https://openalex.org/W3013693939","https://openalex.org/W2010746423","https://openalex.org/W3035935536","https://openalex.org/W2159052453","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W3088732000"],"abstract_inverted_index":{"Technology":[0],"scaling":[1],"has":[2,11,123],"increased":[3],"the":[4,18,41,49,119,165,175,205,214,226,233,239,244,251,256,266],"complexity":[5],"of":[6,20,26,96,109,133,139,167,220,258],"integrated":[7],"circuit":[8],"design.":[9],"It":[10,99],"also":[12],"led":[13],"to":[14,72,102,112,153,156,162,173,197,212,231],"more":[15],"challenges":[16,28],"in":[17,48],"field":[19],"Design":[21],"for":[22,54,179],"Manufacturing":[23],"(DFM).":[24],"One":[25],"these":[27,45,163],"is":[29,52,82,100,171,191],"lithography":[30],"hotspot":[31,61,142],"detection.":[32],"Hotspots":[33],"(HS)":[34],"are":[35,64,195,210,229],"design":[36,50],"patterns":[37,46,110,145,215,228],"that":[38,202,243,262],"negatively":[39],"affect":[40],"output":[42],"yield.":[43],"Identifying":[44],"early":[47],"phase":[51],"crucial":[53],"high":[55,157],"yield":[56],"fabrication.":[57],"Machine":[58],"Learning-based":[59],"(ML)":[60],"detection":[62,121],"techniques":[63,170],"promising":[65],"since":[66],"they":[67],"have":[68,129,147],"shown":[69],"superior":[70],"results":[71],"other":[73],"methods":[74],"such":[75],"as":[76],"pattern":[77],"matching.":[78],"Training":[79],"ML":[80],"models":[81,104,152],"a":[83,130,136,184],"challenging":[84],"task":[85],"due":[86,111],"three":[87],"main":[88],"reasons.":[89],"First,":[90],"industrial":[91],"training":[92,234],"designs":[93],"contain":[94],"millions":[95],"unique":[97],"patterns.":[98,207],"impractical":[101],"train":[103],"using":[105,216,265],"this":[106,182],"large":[107,137],"number":[108,132,138],"limited":[113,131],"computational":[114],"and":[115,135,143,260],"memory":[116],"resources.":[117],"Second,":[118],"HS":[120,134],"problem":[122],"an":[124],"imbalanced":[125],"nature;":[126],"datasets":[127],"typically":[128],"non-hotspots.":[140],"Lastly,":[141],"non-hotspot":[144],"can":[146,203,249],"very":[148],"similar":[149],"geometries":[150],"causing":[151],"be":[154],"susceptible":[155],"false":[158],"positive":[159],"rates.":[160],"Due":[161],"reasons,":[164],"use":[166],"data":[168,200,246],"sampling":[169,186,247],"needed":[172],"choose":[174],"best":[176],"representative":[177],"dataset":[178,185,241,252],"training.":[180],"In":[181],"paper,":[183],"technique":[187],"based":[188],"on":[189,238],"autoencoders":[190,194],"introduced.":[192],"The":[193],"used":[196,211],"identify":[198],"latent":[199],"features":[201,209],"reconstruct":[204],"input":[206],"These":[208],"group":[213],"Density-based":[217],"spatial":[218],"clustering":[219],"applications":[221],"with":[222],"noise":[223],"(DBSCAN).":[224],"Then,":[225],"clustered":[227],"sampled":[230],"reduce":[232,250],"set":[235],"size.":[236],"Experiments":[237],"ICCAD-2019":[240],"show":[242],"proposed":[245],"approach":[248],"size":[253],"while":[254],"maintaining":[255],"levels":[257],"recall":[259],"precision":[261],"were":[263],"obtained":[264],"full":[267],"dataset.":[268]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}