{"id":"https://openalex.org/W4402013294","doi":"https://doi.org/10.1186/s42162-024-00380-w","title":"The application of deep learning technology in integrated circuit design","display_name":"The application of deep learning technology in integrated circuit design","publication_year":2024,"publication_date":"2024-08-29","ids":{"openalex":"https://openalex.org/W4402013294","doi":"https://doi.org/10.1186/s42162-024-00380-w"},"language":"en","primary_location":{"id":"doi:10.1186/s42162-024-00380-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-024-00380-w","pdf_url":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00380-w","source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00380-w","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047136144","display_name":"Lihua Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114178","display_name":"Suzhou Vocational Institute of Industrial Technology","ror":"https://ror.org/027rn2111","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210114178"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lihua Dai","raw_affiliation_strings":["School of Integrated Circuits and Communications, Suzhou Vocational Institute of Industrial Technology, Suzhou, Jiangsu, 215104, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Integrated Circuits and Communications, Suzhou Vocational Institute of Industrial Technology, Suzhou, Jiangsu, 215104, China","institution_ids":["https://openalex.org/I4210114178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100341636","display_name":"Ben Wang","orcid":"https://orcid.org/0000-0002-5341-1642"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ben Wang","raw_affiliation_strings":["Microsoft (China) Co., LTD. Suzhou Branch, Suzhou, Jiangsu, 215000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft (China) Co., LTD. Suzhou Branch, Suzhou, Jiangsu, 215000, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069110387","display_name":"Xuemin Cheng","orcid":"https://orcid.org/0000-0003-2150-776X"},"institutions":[{"id":"https://openalex.org/I4210114178","display_name":"Suzhou Vocational Institute of Industrial Technology","ror":"https://ror.org/027rn2111","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210114178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuemin Cheng","raw_affiliation_strings":["School of Integrated Circuits and Communications, Suzhou Vocational Institute of Industrial Technology, Suzhou, Jiangsu, 215104, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Integrated Circuits and Communications, Suzhou Vocational Institute of Industrial Technology, Suzhou, Jiangsu, 215104, China","institution_ids":["https://openalex.org/I4210114178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115595683","display_name":"Qin Wang","orcid":"https://orcid.org/0009-0004-3954-7879"},"institutions":[{"id":"https://openalex.org/I4210114178","display_name":"Suzhou Vocational Institute of Industrial Technology","ror":"https://ror.org/027rn2111","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210114178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qin Wang","raw_affiliation_strings":["School of Integrated Circuits and Communications, Suzhou Vocational Institute of Industrial Technology, Suzhou, Jiangsu, 215104, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Integrated Circuits and Communications, Suzhou Vocational Institute of Industrial Technology, Suzhou, Jiangsu, 215104, China","institution_ids":["https://openalex.org/I4210114178"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109772086","display_name":"Xinsen Ni","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114178","display_name":"Suzhou Vocational Institute of Industrial Technology","ror":"https://ror.org/027rn2111","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210114178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinsen Ni","raw_affiliation_strings":["School of Integrated Circuits and Communications, Suzhou Vocational Institute of Industrial Technology, Suzhou, Jiangsu, 215104, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Integrated Circuits and Communications, Suzhou Vocational Institute of Industrial Technology, Suzhou, Jiangsu, 215104, China","institution_ids":["https://openalex.org/I4210114178"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5047136144"],"corresponding_institution_ids":["https://openalex.org/I4210114178"],"apc_list":{"value":1340,"currency":"USD","value_usd":1340},"apc_paid":{"value":1340,"currency":"USD","value_usd":1340},"fwci":1.7667,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.84769359,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"7","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10558","display_name":"Advancements in Semiconductor Devices and Circuit Design","score":0.9983999729156494,"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/T10558","display_name":"Advancements in Semiconductor Devices and Circuit Design","score":0.9983999729156494,"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/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9983000159263611,"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/T10472","display_name":"Semiconductor materials and devices","score":0.9979000091552734,"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/deep-learning","display_name":"Deep learning","score":0.7428500652313232},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7298361659049988},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5857003927230835},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5805780291557312},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4876812696456909},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.45011812448501587}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7428500652313232},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7298361659049988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5857003927230835},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5805780291557312},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4876812696456909},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.45011812448501587}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s42162-024-00380-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-024-00380-w","pdf_url":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00380-w","source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:02e7dbadd17a4b2faae0e769740ee044","is_oa":false,"landing_page_url":"https://doaj.org/article/02e7dbadd17a4b2faae0e769740ee044","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Energy Informatics, Vol 7, Iss 1, Pp 1-20 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s42162-024-00380-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-024-00380-w","pdf_url":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00380-w","source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402013294.pdf","grobid_xml":"https://content.openalex.org/works/W4402013294.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W2808706099","https://openalex.org/W2810917770","https://openalex.org/W2889612200","https://openalex.org/W2926076842","https://openalex.org/W2946493845","https://openalex.org/W2966452952","https://openalex.org/W2972527748","https://openalex.org/W2976859966","https://openalex.org/W2980548235","https://openalex.org/W3091512128","https://openalex.org/W3107965887","https://openalex.org/W3135515578","https://openalex.org/W3168549773","https://openalex.org/W3205541443","https://openalex.org/W4205934073","https://openalex.org/W4206024856","https://openalex.org/W4210552504","https://openalex.org/W4214868150","https://openalex.org/W4220998120","https://openalex.org/W4229001679","https://openalex.org/W4285719088","https://openalex.org/W4380049369","https://openalex.org/W4387409473","https://openalex.org/W4388189915","https://openalex.org/W4388300744","https://openalex.org/W4389776171","https://openalex.org/W4390692526","https://openalex.org/W4391468044"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W4226493464","https://openalex.org/W3215138031","https://openalex.org/W4312417841","https://openalex.org/W4306674287","https://openalex.org/W3193565141","https://openalex.org/W3009238340","https://openalex.org/W3133861977","https://openalex.org/W4321369474"],"abstract_inverted_index":{"Abstract":[0],"This":[1],"study":[2],"addresses":[3],"the":[4,18,53,107,132,162,173,186,201,230,242,249],"intricate":[5],"challenge":[6],"of":[7,41,55,109,134,154,188,203,232],"circuit":[8,14,75,254],"layout":[9,76,89,255],"optimization":[10,146,238],"central":[11],"to":[12,60,73,113,180,253],"integrated":[13],"(IC)":[15],"design,":[16],"where":[17],"primary":[19],"goals":[20],"involve":[21],"attaining":[22],"an":[23,70,87,149],"optimal":[24],"balance":[25],"among":[26],"power":[27,151,178],"consumption,":[28],"performance":[29,92],"metrics,":[30],"and":[31,95,101,119,127,156,220],"chip":[32],"area":[33],"(collectively":[34],"known":[35],"as":[36,69],"PPA":[37],"optimization).":[38],"The":[39,140],"complexity":[40],"this":[42],"task,":[43],"evolving":[44],"into":[45],"a":[46,91,96,170],"multidimensional":[47,250],"problem":[48],"under":[49],"multiple":[50,121],"constraints,":[51],"necessitates":[52],"exploration":[54],"advanced":[56],"methodologies.":[57],"In":[58],"response":[59],"these":[61],"challenges,":[62],"our":[63,135],"research":[64],"introduces":[65],"deep":[66,110,137,189,204,233],"learning":[67,111,190,205,234],"technology":[68],"innovative":[71],"strategy":[72],"revolutionize":[74],"optimization.":[77,256],"Specifically,":[78],"we":[79],"employ":[80],"Convolutional":[81],"Neural":[82],"Networks":[83],"(CNNs)":[84],"in":[85,145,172,206,210,235,247],"developing":[86],"optimized":[88],"strategy,":[90],"prediction":[93],"model,":[94],"system":[97],"for":[98,177,244],"fault":[99],"detection":[100],"real-time":[102],"monitoring.":[103],"These":[104,225],"methodologies":[105,191],"leverage":[106],"capacity":[108],"models":[112],"learn":[114],"from":[115],"high-dimensional":[116],"data":[117],"representations":[118],"handle":[120],"constraints":[122],"effectively.":[123],"Extensive":[124],"case":[125],"studies":[126],"rigorous":[128],"experimental":[129],"validations":[130],"demonstrate":[131],"efficacy":[133],"proposed":[136],"learning-driven":[138],"approaches.":[139],"results":[141],"highlight":[142],"significant":[143],"enhancements":[144],"efficiency,":[147],"with":[148],"average":[150,174],"consumption":[152],"reduction":[153,171],"120%":[155],"latency":[157],"decrease":[158],"by":[159,169],"1.5%.":[160],"Furthermore,":[161],"predictive":[163],"capabilities":[164],"are":[165],"markedly":[166],"improved,":[167],"evidenced":[168],"absolute":[175],"error":[176],"predictions":[179],"3%.":[181],"Comparative":[182],"analyses":[183],"conclusively":[184],"illustrate":[185],"superiority":[187],"over":[192],"conventional":[193],"techniques":[194],"across":[195],"several":[196],"dimensions.":[197],"Our":[198],"findings":[199],"underscore":[200],"potential":[202],"achieving":[207],"higher":[208],"accuracy":[209],"predictions,":[211],"demonstrating":[212],"stronger":[213],"generalization":[214],"abilities,":[215],"facilitating":[216],"superior":[217],"design":[218,237],"quality,":[219],"ultimately":[221],"enhancing":[222],"user":[223],"satisfaction.":[224],"advancements":[226,246],"not":[227],"only":[228],"validate":[229],"applicability":[231],"IC":[236],"but":[239],"also":[240],"pave":[241],"way":[243],"future":[245],"addressing":[248],"challenges":[251],"inherent":[252]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
