{"id":"https://openalex.org/W3014565068","doi":"https://doi.org/10.1109/iscas45731.2020.9180722","title":"A Hybrid FeMFET-CMOS Analog Synapse Circuit for Neural Network Training and Inference","display_name":"A Hybrid FeMFET-CMOS Analog Synapse Circuit for Neural Network Training and Inference","publication_year":2020,"publication_date":"2020-09-29","ids":{"openalex":"https://openalex.org/W3014565068","doi":"https://doi.org/10.1109/iscas45731.2020.9180722","mag":"3014565068"},"language":"en","primary_location":{"id":"doi:10.1109/iscas45731.2020.9180722","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas45731.2020.9180722","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2004.00703","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003063931","display_name":"Arman Kazemi","orcid":"https://orcid.org/0000-0002-2009-5516"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arman Kazemi","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002836245","display_name":"Ramin Rajaei","orcid":"https://orcid.org/0000-0003-3851-9396"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramin Rajaei","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075633314","display_name":"Kai Ni","orcid":"https://orcid.org/0000-0002-3628-3431"},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kai Ni","raw_affiliation_strings":["Rochester Institute of Technology","Rochester Institute Of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology","institution_ids":["https://openalex.org/I155173764"]},{"raw_affiliation_string":"Rochester Institute Of Technology","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036105393","display_name":"Suman Datta","orcid":"https://orcid.org/0000-0001-6044-5173"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suman Datta","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003032564","display_name":"Michael Niemier","orcid":"https://orcid.org/0000-0001-7776-4306"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Niemier","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100600905","display_name":"Xiaobo Sharon Hu","orcid":"https://orcid.org/0000-0002-6636-9738"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"X. Sharon Hu","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":1.0,"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":0.9991999864578247,"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":0.6388918161392212},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.554161787033081},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5206400752067566},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.4129818379878998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31482064723968506},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1689949631690979}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6388918161392212},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.554161787033081},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5206400752067566},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.4129818379878998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31482064723968506},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1689949631690979}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/iscas45731.2020.9180722","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas45731.2020.9180722","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2004.00703","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.00703","pdf_url":"https://arxiv.org/pdf/2004.00703","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3014565068","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2004.00703.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2004.00703","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2004.00703","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2004.00703","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.00703","pdf_url":"https://arxiv.org/pdf/2004.00703","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.6700000166893005,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3014565068.pdf","grobid_xml":"https://content.openalex.org/works/W3014565068.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2045322931","https://openalex.org/W2285660444","https://openalex.org/W2307193480","https://openalex.org/W2462963692","https://openalex.org/W2675031852","https://openalex.org/W2786159202","https://openalex.org/W2787759178","https://openalex.org/W2803163155","https://openalex.org/W2885334747","https://openalex.org/W2899077824","https://openalex.org/W2907930361","https://openalex.org/W2912270587","https://openalex.org/W2913902313","https://openalex.org/W2950656546","https://openalex.org/W2954142406","https://openalex.org/W3118608800","https://openalex.org/W6713134421"],"related_works":["https://openalex.org/W1511195938","https://openalex.org/W2089160284","https://openalex.org/W1676984169","https://openalex.org/W2020376324","https://openalex.org/W1532494847","https://openalex.org/W2103230821","https://openalex.org/W2118438044","https://openalex.org/W2854909849","https://openalex.org/W2288196160","https://openalex.org/W2508071702","https://openalex.org/W2884986817","https://openalex.org/W2771447466","https://openalex.org/W2558006273","https://openalex.org/W2298817910","https://openalex.org/W1925364626","https://openalex.org/W2817090607","https://openalex.org/W1763781265","https://openalex.org/W2953875801","https://openalex.org/W2271982470","https://openalex.org/W2917368104"],"abstract_inverted_index":{"An":[0],"analog":[1],"synapse":[2,71,98,134],"circuit":[3,17],"based":[4],"on":[5,75,150],"ferroelectric-metal":[6],"field-effect":[7],"transistors":[8],"is":[9,18,78],"proposed,":[10],"that":[11],"offers":[12,99],"6-bit":[13],"weight":[14],"precision.":[15,94],"The":[16,42],"comprised":[19],"of":[20,65,86,101,120,159],"volatile":[21],"least":[22],"significant":[23,33],"bits":[24,34],"(LSBs)":[25],"used":[26,36],"solely":[27],"during":[28],"training,":[29],"and":[30,40,57,117],"non-volatile":[31],"most":[32],"(MSBs)":[35],"for":[37],"both":[38],"training":[39],"inference.":[41],"design":[43],"works":[44],"at":[45],"a":[46,143],"1.8V":[47],"logic-compatible":[48],"voltage,":[49],"provides":[50],"10":[51],"<sup":[52],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[53],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">10</sup>":[54],"endurance":[55,123],"cycles,":[56,124],"requires":[58],"only":[59,79],"250ps":[60],"update":[61,114],"pulses.":[62],"A":[63],"variant":[64],"LeNet":[66],"trained":[67],"with":[68,90],"the":[69,87,91,96,160],"proposed":[70,97,133],"achieves":[72],"98.2%":[73],"accuracy":[74,149],"MNIST,":[76],"which":[77],"0.4%":[80],"lower":[81,154],"than":[82,155],"an":[83,139,156],"ideal":[84,157],"implementation":[85,158],"same":[88,92,161],"network":[89,145],"bit":[93],"Furthermore,":[95],"improvements":[100],"up":[102],"to":[103,127,138,146],"26%":[104],"in":[105,108,112,122],"area,":[106],"44.8%":[107],"leakage":[109],"power,":[110],"16.7%":[111],"LSB":[113],"pulse":[115],"duration,":[116],"two":[118],"orders":[119],"magnitude":[121],"when":[125],"compared":[126],"state-of-the-art":[128],"hybrid":[129],"synaptic":[130],"circuits.":[131],"Our":[132],"can":[135],"be":[136],"extended":[137],"8-bit":[140],"design,":[141],"enabling":[142],"VGG-like":[144],"achieve":[147],"88.8%":[148],"CIFAR-10":[151],"(only":[152],"0.8%":[153],"network).":[162]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
