Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

246 Pages·2020·31.148 MB·English

by  Jordi Suñé (editor)

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Edited by
Jordi Suñé
Printed Edition of the Special Issue Published in Materials
www.mdpi.com/journal/materials
Memristors for Neuromorphic Circuits
and Artificial Intelligence Applications

Memristors for Neuromorphic Circuits
and Artificial Intelligence Applications
SpecialIssueEditor
JordiSun˜e´
MDPI•Basel•Beijing•Wuhan•Barcelona•Belgrade
SpecialIssueEditor
JordiSun˜e´
UniversitatAuto`nomade
Barcelona,Departament
d‘EnginyeriaElectro`nica
Spain
EditorialOffice
MDPI
St.Alban-Anlage66
4052Basel,Switzerland
ThisisareprintofarticlesfromtheSpecialIssuepublishedonlineintheopenaccessjournalMaterials
(ISSN 1996-1944) from 2018 to 2020 (available at: https://www.mdpi.com/journal/materials/
specialissues/memristors).
Forcitationpurposes,citeeacharticleindependentlyasindicatedonthearticlepageonlineandas
indicatedbelow:
LastName,A.A.; LastName,B.B.; LastName,C.C.ArticleTitle. JournalNameYear,ArticleNumber,
PageRange.
ISBN978-3-03928-576-1(Pbk)
ISBN978-3-03928-577-8(PDF)
Cover image courtesy of Jaime Moroldo, Italian-Venezuelan plastic artist based in Spain, who is a
long-lasting friend of Jordi Sun˜´e, editor of this book.
(cid:2)c 2020 by the authors. Articles in this book are Open Access and distributed under the Creative
Commons Attribution (CC BY) license, which allows users to download, copy and build upon
published articles, as long as the author and publisher areproperly credited, which ensures maximum
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The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons
license CC BY-NC-ND.
Contents
AbouttheSpecialIssueEditor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
EnriqueMirandaandJordiSun˜e´
MemristorsforNeuromorphicCircuitsandArtificialIntelligenceApplications
Reprintedfrom:Materials2020,13,938,doi:10.3390/ma13040938. . . . . . . . . . . . . . . . . . . 1
LuisA.Camun˜as-Mesa,Bernabe´Linares-BarrancoandTeresaSerrano-Gotarredona
Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware
Implementations
Reprintedfrom:Materials2019,12,2745,doi:10.3390/ma12172745 . . . . . . . . . . . . . . . . . . 10
ValerioMilo,GerardoMalavena,ChristianMonzioCompagnoniandDanieleIelmini
MemristiveandCMOSDevicesforNeuromorphicComputing
Reprintedfrom:Materials2020,13,166,doi:10.3390/ma13010166. . . . . . . . . . . . . . . . . . . 38
AlejandroFerna´ndez-Rodr´ıguez,JordiAlcala`,JordiSun˜e,NarcisMestresandAnnaPalau
Multi-Terminal Transistor-Like Devices Based on Strongly Correlated Metallic Oxides for
NeuromorphicApplications
Reprintedfrom:Materials2020,13,281,doi:10.3390/ma13020281. . . . . . . . . . . . . . . . . . . 71
RuiWang,TuoShi,XumengZhang,WeiWang,JinsongWei,JianLu,XiaolongZhao,Zuheng
Wu,RongrongCao,ShibingLong,QiLiuandMingLiu
BipolarAnalogMemristorsasArtificialSynapsesforNeuromorphicComputing
Reprintedfrom:Materials2018,11,2102,doi:10.3390/ma11112102 . . . . . . . . . . . . . . . . . . 82
WookyungSun,SujinChoi,BokyungKimandJunheePark
Three-Dimensional (3D) Vertical Resistive Random-Access Memory (VRRAM) Synapses for
NeuralNetworkSystems
Reprintedfrom:Materials2019,12,3451,doi:10.3390/ma12203451 . . . . . . . . . . . . . . . . . . 96
PaoloLaTorraca,FrancescoMariaPuglisi,AndreaPadovaniandLucaLarcher
Multiscale Modeling for Application-Oriented Optimization of Resistive
Random-AccessMemory
Reprintedfrom:Materials2019,12,3461,doi:10.3390/ma12213461 . . . . . . . . . . . . . . . . . . 108
N. Rodriguez, D. Maldonado, F. J. Romero, F. J. Alonso, A. M. Aguilera, A. Godoy, F.
Jimenez-Molinos,F.G.RuizandJ.B.Roldan
Resistive Switching and Charge Transport in Laser-Fabricated Graphene Oxide Memristors:
ATimeSeriesandQuantumPointContactModelingApproach
Reprintedfrom:Materials2019,12,3734,doi:10.3390/ma12223734 . . . . . . . . . . . . . . . . . . 133
Da´nielHajto´,A´da´mRa´kandGyo¨rgyCserey
RobustMemristorNetworksforNeuromorphicComputationApplications
Reprintedfrom:Materials2019,12,3573,doi:10.3390/ma12213573 . . . . . . . . . . . . . . . . . . 142
SonNgocTruong
A Parasitic Resistance-Adapted Programming Scheme for Memristor Crossbar-Based
NeuromorphicComputingSystems
Reprintedfrom:Materials2019,12,4097,doi:10.3390/ma12244097 . . . . . . . . . . . . . . . . . . 153
v
Agustı´nCisternasFerri,AlanRapoport,GermanPatterson,PabloFierens,EnriqueMiranda
andJordiSun˜e´
OntheApplicationofaDiffusiveMemristorCompactModeltoNeuromorphicCircuits
Reprintedfrom:Materials2019,12,2260,doi:10.3390/ma12142260 . . . . . . . . . . . . . . . . . . 165
Marta Pedro´, Javier Martı´n-Mart´ınez, Marcos Maestro-Izquierdo, Rosana Rodr´ıguez and
MontserratNafrı´a
Self-Organizing Neural Networks Based on OxRAM Devices under a Fully Unsupervised
TrainingScheme
Reprintedfrom:Materials2019,12,3482,doi:10.3390/ma12213482 . . . . . . . . . . . . . . . . . . 183
TienVanNguyen,KhoaVanPhamandKyeong-SikMin
Memristor-CMOS Hybrid Circuit for Temporal-Pooling of Sensory and Hippocampal
ResponsesofCorticalNeurons
Reprintedfrom:Materials2019,12,875,doi:10.3390/ma12060875. . . . . . . . . . . . . . . . . . . 201
TienVanNguyen,KhoaVanPhamandKyeong-SikMin
Hybrid Circuit of Memristor and Complementary Metal-Oxide-Semiconductor for
Defect-TolerantSpatialPoolingwithBoost-FactorAdjustment
Reprintedfrom:Materials2019,12,2122,doi:10.3390/ma12132122 . . . . . . . . . . . . . . . . . . 215
vi
About the Special Issue Editor
Jordi Su˜n´eis a full professor of Electronics at the Universitat Aut`onomad e Barcelona (UAB). He is
the coordinator of the NANOCOMP research group, dedicated to the modeling and simulation of
electron devices with a multi-scale approach. His main contributions are in the area of gate oxide
reliability for CMOS technology. In terms of research achievements in this field, he was upgraded to
IEEE Fellow for contributions to the understanding of gate oxide failure and reliability methodology.
In 2008, he received the IBM Faculty award for a long-lasting collaboration with IBM
Microelectronics in this field. Since 2008, he has worked in the area of memristive devices and their
application to neuromorphic circuits. In 2010, he received the ICREA ACADEMIA award and, in
2012 and 2013, he was awarded the Chinese Academy of Sciences Professorship for Senior
International Scientists, for a collaboration with IMECAS (Beijing, China). Recently, he launched a
new research group/network (neuromimeTICs.org) dedicated to the application of neuromorphic
electronics to artificial intelligence and to dissemination activities. He has (co)authored more than 400
papers (h-index = 44) in international journals and relevant conferences, including 14 IEDM papers,
several invited papers, and five tutorials on oxide reliability at the IEEE-IRPS. At present, he’s the
local UAB coordinator of a European project (EU2020-ECSEL-WAKeMeUP) on emerging non-volatile
memories embedded in microprocessors for automotive, secure, and general electronics applications.
His present research interests are: transition metal oxide-based filamentary RRAM memristors;
complex perovskite oxide based memristors; bio-realistic compact modeling of memristors for
neuromorphic applications; RRAM fabrication, characterization and modelling; biomimetic electrical
circuit simulation with SPICE; analog circuits based on the combination of CMOS and memristors;
and, in general, artificial neural networks for artificial intelligence applications. Jordi Suñé was
funded by the WAKeMeUP 783176 project, co-funded by grants from the Spanish Ministerio de
Ciencia, Innovación y Universidades (PCI2018-093107 grant) and the ECSEL EU Joint Undertaking.
vii

Preface to ”Memristors for Neuromorphic Circuits and
Artificial Intelligence Applications”
Theapplicationsofartificialintelligence(AI)andtheirimpactsonglobalsocietyarecurrently
growingatanexponentialpace.Imageandspeechrecognitionandprocessing,businessoptimization,
medical diagnosis, autonomous cars, and science discovery are only some of these applications.
Although the term AI was coined in the late 1950s, it is only in the past decade that due to
the impressive improvements in computing power, AI has found applications in many areas,
evenexceedinghumansinsometasks.Itisconvenienttodistinguishbetweenconventional(narrow)
AIapplicationsdesignedforonespecifictask,andartificialgeneralintelligence(AGI),whichaims
atemulatinghumansinthemostgeneralsituations. AllbigAIindustrialplayersarecommittedto
achievingAGIwiththeideathatonceyousolveintelligence,youcanuseittosolveeverythingelse.
AIisheraldedasarevolutionarytechnologyforthe21stcentury; ithasmanyapplicationsforthe
goodbut,initsAGIversion,ithasalsobeensignaledasoneofthesignificantrisksforthefutureof
humanity.Artificialneuralnetworks(ANNs)areinspiredbythestructureofthebrainandareformed
by artificial neurons interconnected by artificial synapses exhibiting plasticity effects. During the
trainingofanANN,largeamountsofdataareprovidedthroughtheinputneuronsandthestrength
ofthesynapticinterconnectionsareprogressivelymodifieduntilthenetworklearnshowtoclassify
notonlythetrainingdatabutalsounforeseendataofasimilarkind. MostAIalgorithmshavebeen
implementedbysoftwareprogramsrunonconventionalcomputingsystemswithaVonNeumann
architecture, suchascentralprocessingunits, graphicalprocessingunits, andfieldprogrammable
gatearrays.Recently,speciallydesignedintegratedcircuits,suchasthetensorprocessingunit(TPU),
havebeenintroducedtooptimizethetypeofoperations(vector-matrixmultiplication)requiredfor
trainingandinference.Inthesecomputingsystems,thememoryandprocessingunitsarephysically
separatedsothatsignificantamountsofdataneedtobeshuttledbackandforthduringcomputation.
Thiscreatesaperformancebottleneckbothinprocessingspeedduetothememorylatencyandin
powerconsumptionduetotheenergyrequirementsfordataretrieval,transportation,andstorage.
Theproblemisaggravatedbydeeplearningsystemsgrowingsignificantlyincomplexityforbetter
recognitionaccuracy; asaconsequence, trainingtime, cost, andenergyconsumptionsignificantly
increase.Thistrendhasthedrawbackthattheover-the-airdistributionrequiredbyedgeapplications
becomes moredifficult, if notimpossible. Giventhe requiredcomplexity ofcomputing resources
andthehugeamountsofenergyconsumed,alternativeapproachestosoftware-basedAItoolsare
urgently needed. In this regard, the hardware realization of AI applications and, in particular,
theuseofmemristorstoimplementANNsmightbethenextstepinthewaytowardfast,compact,
andenergyefficientAIsystemswithaperformancemuchclosertothatofthehumanbrain.
In this book, various reputed authors cover different aspects of the implementation of these
memristiveneuromorphicsystems. Thebookstartswithaneditorialandtwoinvitedcontributions
that review the state-of-the-art ANNs implemented in hardware and present the basic concepts
of neuromorphics. After this, different papers cover the whole field by focusing on advanced
memristor devices for synaptic applications, including modelling for device improvement and
circuit simulation; on some issues related to the organization of memristors in dense crossbar
arrays, and finally on the application of these circuits to AI problems. The device-related papers
cover promising three-terminal structures based on complex perovskite oxides, devices based on
binaryoxidesandnewtrainingprotocolstoachieveimprovedsynapticproperties,andnewvertical
ix