{"id":"https://openalex.org/W3161606026","doi":"https://doi.org/10.1109/irps46558.2021.9405228","title":"Characterization and Mitigation of Relaxation Effects on Multi-level RRAM based In-Memory Computing","display_name":"Characterization and Mitigation of Relaxation Effects on Multi-level RRAM based In-Memory Computing","publication_year":2021,"publication_date":"2021-03-01","ids":{"openalex":"https://openalex.org/W3161606026","doi":"https://doi.org/10.1109/irps46558.2021.9405228","mag":"3161606026"},"language":"en","primary_location":{"id":"doi:10.1109/irps46558.2021.9405228","is_oa":false,"landing_page_url":"https://doi.org/10.1109/irps46558.2021.9405228","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Reliability Physics Symposium (IRPS)","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/A5018917330","display_name":"Wangxin He","orcid":"https://orcid.org/0000-0003-1882-545X"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wangxin He","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032184694","display_name":"Wonbo Shim","orcid":"https://orcid.org/0000-0002-9669-7310"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wonbo Shim","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074272474","display_name":"Shihui Yin","orcid":"https://orcid.org/0000-0001-7186-0946"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shihui Yin","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062894678","display_name":"Xiaoyu Sun","orcid":"https://orcid.org/0000-0001-5337-5680"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoyu Sun","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047916979","display_name":"Deliang Fan","orcid":"https://orcid.org/0000-0002-7989-6297"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deliang Fan","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054894631","display_name":"Shimeng Yu","orcid":"https://orcid.org/0000-0002-0068-3652"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shimeng Yu","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007690955","display_name":"Jae-sun Seo","orcid":"https://orcid.org/0000-0002-4551-7789"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jae-sun Seo","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5018917330"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":0.8022,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.71212777,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"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/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":0.9995999932289124,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/resistive-random-access-memory","display_name":"Resistive random-access memory","score":0.968737006187439},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7332209348678589},{"id":"https://openalex.org/keywords/spice","display_name":"Spice","score":0.7027283310890198},{"id":"https://openalex.org/keywords/relaxation","display_name":"Relaxation (psychology)","score":0.6557333469390869},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5928162336349487},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46357208490371704},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.46068575978279114},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4346921145915985},{"id":"https://openalex.org/keywords/resistive-touchscreen","display_name":"Resistive touchscreen","score":0.42780429124832153},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.3586452007293701},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32091107964515686},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27026689052581787},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.18591198325157166},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16869422793388367},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12337058782577515}],"concepts":[{"id":"https://openalex.org/C182019814","wikidata":"https://www.wikidata.org/wiki/Q1143830","display_name":"Resistive random-access memory","level":3,"score":0.968737006187439},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7332209348678589},{"id":"https://openalex.org/C2780077345","wikidata":"https://www.wikidata.org/wiki/Q16891888","display_name":"Spice","level":2,"score":0.7027283310890198},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.6557333469390869},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5928162336349487},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46357208490371704},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.46068575978279114},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4346921145915985},{"id":"https://openalex.org/C6899612","wikidata":"https://www.wikidata.org/wiki/Q852911","display_name":"Resistive touchscreen","level":2,"score":0.42780429124832153},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3586452007293701},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32091107964515686},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27026689052581787},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.18591198325157166},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16869422793388367},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12337058782577515},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/irps46558.2021.9405228","is_oa":false,"landing_page_url":"https://doi.org/10.1109/irps46558.2021.9405228","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Reliability Physics Symposium (IRPS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2735902081","display_name":null,"funder_award_id":"1652866/1715443/1740225","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2285428150","https://openalex.org/W2526343563","https://openalex.org/W2787475303","https://openalex.org/W2974585810","https://openalex.org/W2999356938","https://openalex.org/W3005885031","https://openalex.org/W3012561096","https://openalex.org/W3015980402","https://openalex.org/W3015982917","https://openalex.org/W3016048022","https://openalex.org/W3017267458","https://openalex.org/W3045216746","https://openalex.org/W3061567197"],"related_works":["https://openalex.org/W2204879205","https://openalex.org/W2096437374","https://openalex.org/W1943174035","https://openalex.org/W2054635671","https://openalex.org/W1928481607","https://openalex.org/W3195722497","https://openalex.org/W2167784970","https://openalex.org/W1980301972","https://openalex.org/W2587448511","https://openalex.org/W2199653281"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"investigate":[4],"the":[5,33,46,55,61,88,98,101,114,120,123,134,142],"relaxation":[6,34,47,115],"effects":[7,35],"on":[8,39],"multi-level":[9],"resistive":[10],"random":[11],"access":[12],"memory":[13],"(RRAM)":[14],"based":[15],"in-memory":[16],"computing":[17],"(IMC)":[18],"for":[19,74,104,150],"deep":[20],"neural":[21],"network":[22],"(DNN)":[23],"inference.":[24],"We":[25,59],"characterized":[26],"2-bit-per-cell":[27],"RRAM":[28,105],"IMC":[29,106],"prototypes":[30],"and":[31,67],"measured":[32],"over":[36],"100":[37],"hours":[38,112],"multiple":[40],"8":[41],"kb":[42],"test":[43],"chips,":[44],"where":[45],"is":[48,116],"found":[49],"to":[50,81,130,133,147],"be":[51,108,131,145],"most":[52],"severe":[53],"in":[54],"two":[56,135],"intermediate":[57,136],"states.":[58,137],"incorporated":[60],"experimental":[62],"data":[63],"into":[64],"SPICE":[65],"simulation":[66],"software":[68],"DNN":[69,72],"inference,":[70],"showing":[71],"accuracy":[73,143],"CIFAR-10":[75],"dataset":[76],"could":[77,107,144],"degrade":[78],"from":[79],"87.35%":[80],"11.58%":[82],"after":[83,110],"144":[84],"hours.":[85],"To":[86],"recover":[87],"largely":[89],"degraded":[90],"accuracy,":[91],"mitigation":[92],"schemes":[93,140],"are":[94,125],"proposed:":[95],"1)":[96],"at":[97],"circuit":[99],"level,":[100,122],"reference":[102],"voltage":[103],"calibrated":[109],"80":[111],"when":[113],"saturated.":[117],"2)":[118],"At":[119],"algorithm":[121],"weights":[124],"trained":[126],"with":[127],"lower":[128],"percentages":[129],"quantized":[132],"With":[138],"both":[139],"applied,":[141],"recovered":[146],"87.32":[148],"%":[149],"long-term":[151],"stability.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}