Nanograin network memory with reconfigurable percolation paths for synaptic interactions

Hoo-Cheol Lee; Jungkil Kim; Ha-Reem Kim; Kyoung-Ho Kim; Kyung-Jun Park; Jae-Pil So; Jung Min Lee; Min-Soo Hwang; Hong-Gyu Park

doi:10.1038/s41377-023-01168-5

Go to Nature Website

Your Location：

Home >

Browse articles >

Nanograin network memory with reconfigurable percolation paths for synaptic interactions

Original Articles

- Nanograin network memory with reconfigurable percolation paths for synaptic interactions
- Nanograin network memory with reconfigurable percolation paths for synaptic interactions
- 光：科学与应用 2023年12卷第6期页码：1040-1050
- Affiliations：
  
  1.Department of Physics, Korea University, Seoul 02841, Republic of Korea
  2.Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
  3.Department of Physics, Chungbuk National University, Cheongju 28644, Republic of Korea
- Author bio：
  
  Jungkil Kim (jungkil@jejunu.ac.kr)
  Hong-Gyu Park (hgpark@korea.ac.kr)
- Funds：
- DOI：10.1038/s41377-023-01168-5
  中图分类号：
- 纸质出版日期：2023-6-30，
  
  网络出版日期：2023-5-15，
  
  收稿日期：2022-12-10，
  
  修回日期：2023-4-15，
  
  录用日期：2023-4-23
Scan for full text
Nanograin network memory with reconfigurable percolation paths for synaptic interactions[J]. 光：科学与应用, 2023,12(6):1040-1050.

Lee, H. C. et al. Nanograin network memory with reconfigurable percolation paths for synaptic interactions. Light: Science & Applications, 12, 1040-1050 (2023).
Nanograin network memory with reconfigurable percolation paths for synaptic interactions[J]. 光：科学与应用, 2023,12(6):1040-1050. DOI： 10.1038/s41377-023-01168-5.

Lee, H. C. et al. Nanograin network memory with reconfigurable percolation paths for synaptic interactions. Light: Science & Applications, 12, 1040-1050 (2023). DOI： 10.1038/s41377-023-01168-5.

摘要

Abstract

The development of memory devices with functions that simultaneously process and store data is required for efficient computation. To achieve this

artificial synaptic devices have been proposed because they can construct hybrid networks with biological neurons and perform neuromorphic computation. However

irreversible aging of these electrical devices causes unavoidable performance degradation. Although several photonic approaches to controlling currents have been suggested

suppression of current levels and switching of analog conductance in a simple photonic manner remain challenging. Here

we demonstrated a nanograin network memory using reconfigurable percolation paths in a single Si nanowire with solid core/porous shell and pure solid core segments. The electrical and photonic control of current percolation paths enabled the analog and reversible adjustment of the persistent current level

exhibiting memory behavior and current suppression in this single nanowire device. In addition

the synaptic behaviors of memory and erasure were demonstrated through potentiation and habituation processes. Photonic habituation was achieved using laser illumination on the porous nanowire shell

with a linear decrease in the postsynaptic current. Furthermore

synaptic elimination was emulated using two adjacent devices interconnected on a single nanowire. Therefore

electrical and photonic reconfiguration of the conductive paths in Si nanograin networks will pave the way for next-generation nanodevice technologies.

关键词

Keywords

references

Ham,D.etal.Neuromorphicelectronicsbasedoncopyingandpastingthebrain.Nat. Electron.4,635–644(2021)..

Marković,D.etal.Physicsforneuromorphiccomputing.Nat. Rev. Phys.2,499–510(2020)..

Merolla,P.A.etal.Amillionspiking-neuronintegratedcircuitwithascalablecommunicationnetworkandinterface.Science345,668–673(2014)..

Zahedinejad,M.etal.Memristivecontrolofmutualspinhallnano-oscillatorsynchronizationforneuromorphiccomputing.Nat. Mater.21,81–87(2022)..

Sangwan,V.K.&Hersam,M.C.Neuromorphicnanoelectronicmaterials.Nat. Nanotechnol.15,517–528(2020)..

Zidan,M.A.,Strachan,J.P.&Lu,W.D.Thefutureofelectronicsbasedonmemristivesystems.Nat. Electron.1,22–29(2018)..

Danial,L.etal.Two-terminalfloating-gatetransistorswithalow-powermemristiveoperationmodeforanalogueneuromorphiccomputing.Nat. Electron.2,596–605(2019)..

Zhu,X.J.etal.IonicmodulationandioniccouplingeffectsinMoS2devicesforneuromorphiccomputing.Nat. Mater.18,141–148(2019)..

Abbott,J.etal.Ananoelectrodearrayforobtainingintracellularrecordingsfromthousandsofconnectedneurons.Nat. Biomed. Eng.4,232–241(2020)..

Custer,J.P.Jretal.Ratchetingquasi-ballisticelectronsinsilicongeometricdiodesatroomtemperature.Science368,177–180(2020)..

Rastogi,S.K.etal.Remotenongeneticopticalmodulationofneuronalactivityusingfuzzygraphene.Proc. Natl Acad. Sci. USA117,13339–13349(2020)..

Wang,Z.R.etal.Memristorswithdiffusivedynamicsassynapticemulatorsforneuromorphiccomputing.Nat. Mater.16,101–108(2017)..

Kim,S.etal.Experimentaldemonstrationofasecond-ordermemristoranditsabilitytobiorealisticallyimplementsynapticplasticity.Nano Lett.15,2203–2211(2015)..

Fuller,E.J.etal.Parallelprogrammingofanionicfloating-gatememoryarrayforscalableneuromorphiccomputing.Science364,570–574(2019)..

Ielmini,D.&Wong,H.S.P.In-memorycomputingwithresistiveswitchingdevices.Nat. Electron.1,333–343(2018)..

Sheridan,P.M.etal.Sparsecodingwithmemristornetworks.Nat. Nanotechnol.12,784–789(2017)..

Wan,W.E.etal.Acompute-in-memorychipbasedonresistiverandom-accessmemory.Nature608,504–512(2022)..

Li,C.etal.Analoguesignalandimageprocessingwithlargememristorcrossbars.Nat. Electron.1,52–59(2018)..

Arita,M.etal.Switchingoperationanddegradationofresistiverandomaccessmemorycomposedoftungstenoxideandcopperinvestigatedusingin-situTEM.Sci. Rep.5,17103(2015)..

Kim,J.etal.Photon-triggerednanowiretransistors.Nat. Nanotechnol.12,963–968(2017)..

Konstantatos,G.etal.Hybridgraphene-quantumdotphototransistorswithultrahighgain.Nat. Nanotechnol.7,363–368(2012)..

Claire,F.J.etal.Structuralandelectronicswitchingofasinglecrystal2Dmetal-organicframeworkpreparedbychemicalvapordeposition.Nat. Commun.11,5524(2020)..

Kang,P.etal.Crumpledgraphenephotodetectorwithenhanced,strain-tunable,andwavelength-selectivephotoresponsivity.Adv. Mater.28,4639–4645(2016)..

Ha,S.T.etal.Synthesisoforganic-inorganicleadhalideperovskitenanoplatelets:towardshigh-performanceperovskitesolarcellsandoptoelectronicdevices.Adv. Opt. Mater.2,838–844(2014)..

Zhou,F.C.etal.Optoelectronicresistiverandomaccessmemoryforneuromorphicvisionsensors.Nat. Nanotechnol.14,776–782(2019)..

Lee,J.etal.Monolayeropticalmemorycellsbasedonartificialtrap-mediatedchargestorageandrelease.Nat. Commun.8,14734(2017)..

Roy,K.etal.Graphene-MoS2hybridstructuresformultifunctionalphotoresponsivememorydevices.Nat. Nanotechnol.8,826–830(2013)..

Yu,J.R.etal.Bioinspiredmechano-photonicartificialsynapsebasedongraphene/MoS2heterostructure.Sci. Adv.7,eabd9117(2021)..

Hamilton,B.etal.Size-controlledpercolationpathwaysforelectricalconductioninporoussilicon.Nature393,443–445(1998)..

Godefroo,S.etal.ClassificationandcontroloftheoriginofphotoluminescencefromSinanocrystals.Nat. Nanotechnol.3,174–178(2008)..

Ben-Chorin,M.,Möller,F.&Koch,F.Nonlinearelectricaltransportinporoussilicon.Phys. Rev. B49,2981–2984(1994)..

Kirkpatrick,S.Percolationandconduction.Rev. Mod. Phys.45,574–588(1973)..

Baek,E.etal.Intrinsicplasticityofsiliconnanowireneurotransistorsfordynamicmemoryandlearningfunctions.Nat. Electron.3,398–408(2020)..

Park,S.O.etal.Experimentaldemonstrationofhighlyreliabledynamicmemristorforartificialneuronandneuromorphiccomputing.Nat. Commun.13,2888(2022)..

Huh,W.etal.Synapticbarristorbasedonphase-engineered2Dheterostructures.Adv. Mater.30,1801447(2018)..

Xi,F.B.etal.Artificialsynapsesbasedonferroelectricschottkybarrierfield-effecttransistorsforneuromorphicapplications.ACS Appl. Mater. Interfaces13,32005–32012(2021)..

Lecun,Y.etal.Gradient-basedlearningappliedtodocumentrecognition.Proc. IEEE86,2278–2324(1998)..

Yasuda,M.etal.Anactivity-dependentdeterminantofsynapseeliminationinthemammalianbrain.Neuron109,1333–1349.e6(2021)..

Yin,L.etal.Synapticsilicon-nanocrystalphototransistorsforneuromorphiccomputing.Nano Energy63,103859(2019)..

Kim,J.etal.Photon-triggeredcurrentgenerationinchemically-synthesizedsiliconnanowires.Nano Lett.19,1269–1274(2019)..

Yao,J.,Yan,H.&Lieber,C.M.Ananoscalecombingtechniqueforthelarge-scaleassemblyofhighlyalignednanowires.Nat. Nanotechnol.8,329–335(2013)..

vandeBurgt,Y.etal.Anon-volatileorganicelectrochemicaldeviceasalow-voltageartificialsynapseforneuromorphiccomputing.Nat. Mater.16,414–418(2017)..

Fuller,E.J.etal.Li-ionsynaptictransistorforlowpoweranalogcomputing.Adv. Mater.29,1604310(2017)..

Ben-Chorin,M.,Möller,F.&Koch,F.Bandalignmentandcarrierinjectionattheporous-silicon-crystalline-siliconinterface.J. Appl. Phys.77,4482–4488(1995)..

Nichols,K.G.&Vernon,E.V.Space-charge-limitedcurrentsinsemiconductorsandinsulators.Majoritycarriertransistors.inTransistorPhysics(edsNichols,K.G.&Vernon,E.V.)(Dordrecht,1966).

Fauchet,P.M.etal.Thepropertiesoffreecarriersinamorphoussilicon.J. Non-Cryst. Solids141,76–87(1992)..

Gleskova,H.etal.Electricalresponseofamorphoussiliconthin-filmtransistorsundermechanicalstrain.J. Appl. Phys.92,6224–6229(2002)..

Lee,H.C.etal.Uniquescatteringpropertiesofsiliconnanowiresembeddedwithporoussegments.ACS Appl. Mater. Interfaces11,21094–21099(2019)..

Jacobs-Gedrim,R.B.etal.AnaloghighresistancebilayerRRAMdeviceforhardwareaccelerationofneuromorphiccomputation.J. Appl. Phys.124,202101(2018)..

Agarwal,S.etal.Resistivememorydevicerequirementsforaneuralalgorithmaccelerator.Proceedings of 2016 International Joint Conference on Neural Networks.p.929-938(IEEE,2016).

Burr,G.W.etal.Experimentaldemonstrationandtolerancingofalarge-scaleneuralnetwork(165000synapses)usingphase-changememoryasthesynapticweightelement.IEEE Trans. Electron Dev.62,3498–3507(2015)..

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Light | 具有可重新配置的突触相互作用渗滤路径的纳米颗粒网络存储器

暂无数据