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Oxide-based RRAM materials for neuromorphic computing

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Abstract

In this review, a comprehensive survey of different oxide-based resistive random-access memories (RRAMs) for neuromorphic computing is provided. We begin with the history of RRAM development, physical mechanism of conduction, fundamental of neuromorphic computing, followed by a review of a variety of RRAM oxide materials (PCMO, HfOx, TaOx, TiOx, NiOx, etc.) with a focus on their application for neuromorphic computing. Our goal is to give a broad review of oxide-based RRAM materials that can be adapted to neuromorphic computing and to help further ongoing research in the field.

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References

  1. Hickmott TW (1962) Low-frequency negative resistance in thin anodic oxide films. J Appl Phys 33(9):2669–2682. https://doi.org/10.1063/1.1702530

    Article  Google Scholar 

  2. Liu SQ, Wu NJ, Ignatiev A (2000) Electric-pulse-induced reversible resistance change effect in magnetoresistive films. Appl Phys Lett 76(19):2749–2751. https://doi.org/10.1063/1.126464

    Article  Google Scholar 

  3. Mandal S, El-Amin A, Alexander K, Rajendran B, Jha R (2014) Novel synaptic memory device for neuromorphic computing. Sci Rep. https://doi.org/10.1038/srep05333

    Google Scholar 

  4. Lee MJ, Park Y, Kang BS, Ahn SE, Lee C, Kim K, Xianyu WX, Stefanovich G, Lee JH, Chung SJ, Kim YH, Lee CS, Park JB, Baek IG, Yoo IK (2007) 2-Stack 1D-1R cross-point structure with oxide diodes as switch elements for high density resistance RAM applications. In: International electron devices meeting, 2007, pp 771–774. https://doi.org/10.1109/iedm.2007.4419061

  5. Strukov DB, Snider GS, Stewart DR, Williams RS (2008) The missing memristor found. Nature 453(7191):80–83. https://doi.org/10.1038/nature06932

    Article  Google Scholar 

  6. Chevallier CJ, Siau CH, Lim SF, Namala SR, Matsuoka M, Bateman BL, Rinerson D (2010) A 0.13 µm 64 Mb multi-layered conductive metal-oxide memory. In: 2010 IEEE international solid-state circuits conference—(ISSCC), pp 260–261

  7. Liu TY, Yan TH, Scheuerlein R, Chen YC, Lee JK, Balakrishnan G, Yee G, Zhang H, Yap A, Ouyang JW, Sasaki T, Al-Shamma A, Chen CY, Gupta M, Hilton G, Kathuria A, Lai V, Matsumoto M, Nigam A, Pai A, Pakhale J, Siau CH, Wu XX, Yin YB, Nagel N, Tanaka Y, Higashitani M, Minvielle T, Gorla C, Tsukamoto T, Yamaguchi T, Okajima M, Okamura T, Takase S, Inoue H, Fasoli L (2014) A 130.7-mm(2) 2-layer 32-Gb ReRAM memory device in 24-nm technology. IEEE J Solid-State Circuits 49(1):140–153

    Article  Google Scholar 

  8. Fackenthal R, Kitagawa M, Otsuka W, Prall K, Mills D, Tsutsui K, Javanifard J, Tedrow K, Tsushima T, Shibahara Y, Hush G (2014) A 16 Gb ReRAM with 200 MB/s write and 1 GB/s read in 27 nm technology. In: ISSCC digest technical papers IEEE international, vol 57, pp 338–339

  9. Luo Q, Xu X, Liu H, Lv H, Gong T, Long S, Liu Q, Sun H, Banerjee W, Li L, Gao J, Lu N, Liu M (2016) Super non-linear RRAM with ultra-low power for 3D vertical nano-crossbar arrays. Nanoscale 8(34):15629–15636. https://doi.org/10.1039/C6NR02029A

    Article  Google Scholar 

  10. Mertens R (2017) Digitimes. http://www.digitimes.com/newregister/join.asp?view=Article&DATEPUBLISH=2017/06/05&PAGES=PB&SEQ=200. June 08, 2017

  11. Chang T-C, Chang K-C, Tsai T-M, Chu T-J, Sze SM (2016) Resistance random access memory. Mater Today 19(5):254–264. https://doi.org/10.1016/j.mattod.2015.11.009

    Article  Google Scholar 

  12. Deng L, Li G, Deng N, Wang D, Zhang Z, He W, Li H, Pei J, Shi L (2015) Complex learning in bio-plausible memristive networks. Sci Rep 5:10684. https://doi.org/10.1038/srep10684

    Article  Google Scholar 

  13. Advances and Trends of RRAM technology SemiconTaiwan (2015)

  14. Wong HSP, Lee HY, Yu SM, Chen YS, Wu Y, Chen PS, Lee B, Chen FT, Tsai MJ (2012) Metal-oxide RRAM. Proc IEEE 100(6):1951–1970. https://doi.org/10.1109/Jproc.2012.2190369

    Article  Google Scholar 

  15. Kuzum D, Yu SM, Wong HSP (2013) Synaptic electronics: materials, devices and applications. Nanotechnology. https://doi.org/10.1088/0957-4484/24/38/382001

    Google Scholar 

  16. Akinaga H, Shima H (2010) Resistive random access memory (ReRAM) based on metal oxides. Proc IEEE 98(12):2237–2251. https://doi.org/10.1109/Jproc.2010.2070830

    Article  Google Scholar 

  17. Waser R, Aono M (2007) Nanoionics-based resistive switching memories. Nat Mater 6(11):833–840. https://doi.org/10.1038/nmat2023

    Article  Google Scholar 

  18. Chi P, Li SC, Xu C, Zhang T, Zhao JS, Liu YP, Wang Y, Xie Y (2016) PRIME: a novel processing-in-memory architecture for neural network computation in ReRAM-based main memory. In: Conference proceedings of international symposium coastal engineering, pp 27–39. https://doi.org/10.1109/isca.2016.13

  19. Qian K, Nguyen VC, Chen TP, Lee PS (2016) Novel concepts in functional resistive switching memories. J Mater Chem C 4(41):9637–9645. https://doi.org/10.1039/c6tc03447k

    Article  Google Scholar 

  20. Tsunoda K, Kinoshita K, Noshiro H, Yarnazaki Y, Iizuka T, Ito Y, Takahashi A, Okano A, Sato Y, Fukano T, Aoki M, Sugiyama Y (2007) Low power and high speed switching of Ti-doped NiOReRAM under the unipolar voltage source of less than 3 V. In: International electron devices meeting, pp 767–770. https://doi.org/10.1109/iedm.2007.4419060

  21. Wang ZR, Zhu WG, Du AY, Wu L, Fang Z, Tran XA, Liu WJ, Zhang KL, Yu HY (2012) Highly uniform, self-compliance, and forming-free ALD HfO2-based RRAM with Ge doping. IEEE Trans Electron Dev 59(4):1203–1208. https://doi.org/10.1109/Ted.2012.2182770

    Article  Google Scholar 

  22. Magyari-Kope B, Duncan D, Zhao L, Nishi Y (2016) Doping technology for RRAM—opportunities and challenges. In: International symposium on VLSI technology

  23. Yu S, Wu Y, Chai Y, Provine J, Wong HSP (2011) Characterization of switching parameters and multilevel capability in HfOx/AlOx bi-layer RRAM devices. In: VLSI technology (VLSIT), 2011 symposium on, pp 106–107

  24. Park SG, Yang MK, Ju H, Seong DJ, Lee JM, Kim E, Jung S, Zhang L, Shin YC, Baek IG, Choi J, Kang HK, Chung C (2012) A non-linear ReRAM cell with sub-1 mu a ultralow operating current for high density vertical resistive memory (VRRAM). In: 2012 IEEE international electron devices meeting (IEDM)

  25. Yun JB, Kim S, Seo S, Lee MJ, Kim DC, Ahn SE, Park Y, Kim J, Shin H (2007) Random and localized resistive switching observation in Pt/NiO/Pt. Phys Status Solidi-R 1(6):280–282. https://doi.org/10.1002/pssr.200701205

    Article  Google Scholar 

  26. Ho PWC, Hatem FO, Almurib HAF, Kumar TN (2016) Comparison between Pt/TiO2/Pt and Pt/TaOX/TaOY/Pt based bipolar resistive switching devices. J Semicond. https://doi.org/10.1088/1674-4926/37/6/064001

    Google Scholar 

  27. Lei XY, Liu HX, Gao HX, Yang HN, Wang GM, Long SB, Ma XH, Liu M (2014) Resistive switching characteristics of Ti/ZrO2/Pt RRAM device. Chin Phys B. https://doi.org/10.1088/1674-1056/23/11/117305

    Google Scholar 

  28. Long SB, Lian XJ, Ye TC, Cagli C, Perniola L, Miranda E, Liu M, Sune J (2013) Cycle-to-cycle intrinsic RESET statistics in HfO2-based unipolar RRAM devices. IEEE Electron Dev Lett 34(5):623–625. https://doi.org/10.1109/Led.2013.2251314

    Article  Google Scholar 

  29. Yoshida C, Tsunoda K, Noshiro H, Sugiyama Y (2007) High speed resistive switching in Pt/TiO2/TiN film for nonvolatile memory application. Appl Phys Lett. https://doi.org/10.1063/1.2818691

    Google Scholar 

  30. Xu N, Liu LF, Sun X, Chen C, Wang Y, Han DD, Liu XY, Han RQ, Kang JF, Yu B (2008) Bipolar switching behavior in TiN/ZnO/Pt resistive nonvolatile memory with fast switching and long retention. Semicond Sci Technol. https://doi.org/10.1088/0268-1242/23/7/075019

    Google Scholar 

  31. Kim S, Lee D, Park J, Jung S, Lee W, Shin J, Woo J, Choi G, Hwang H (2012) Defect engineering: reduction effect of hydrogen atom impurities in HfO2-based resistive-switching memory devices. Nanotechnology. https://doi.org/10.1088/0957-4484/23/32/325702

    Google Scholar 

  32. Ma G, Tang X, Zhang H, Zhong Z, Li X, Li J, Su H (2017) Ultra-high ON/OFF ratio and multi-storage on NiO resistive switching device. J Mater Sci 52(1):238–246. https://doi.org/10.1007/s10853-016-0326-5

    Article  Google Scholar 

  33. Sarkar PK, Prajapat M, Barman A, Bhattacharjee S, Roy A (2016) Multilevel resistance state of Cu/La2O3/Pt forming-free switching devices. J Mater Sci 51(9):4411–4418. https://doi.org/10.1007/s10853-016-9753-6

    Article  Google Scholar 

  34. Tsai CL, Xiong F, Pop E, Shim M (2013) Resistive random access memory enabled by carbon nanotube crossbar electrodes. ACS Nano 7(6):5360–5366. https://doi.org/10.1021/nn401212p

    Article  Google Scholar 

  35. Wu Y, Chai Y, Chen H-Y, Yu S, Wong H-SP (2011) Resistive switching AlOx-based memory with CNT electrode for ultra-low switching current and high density memory application. In: VLSI technology (VLSIT), 2011 Symposium on

  36. Munoz-Gorriz J, Acero MC, Gonzalez MB, Campabadal F (2017) Top electrode dependence of the resistive switching behavior in HfO2/n(+)Si-based devices. In: Spanish conference electron

  37. Sohn J, Lee S, Jiang ZZ, Chen HY, Wong HSP (2014) Atomically thin graphene plane electrode for 3D RRAM. In: 2014 IEEE international electron devices meeting (IEDM)

  38. Cheng CH, Chin A, Yeh FS (2011) Ultralow switching energy Ni/GeOx/HfON/TaN RRAM. IEEE Electron Device Lett 32(3):366–368. https://doi.org/10.1109/Led.2010.2095820

    Article  Google Scholar 

  39. Yoon J, Choi H, Lee D, Park JB, Lee J, Seong DJ, Ju Y, Chang M, Jung S, Hwang H (2009) Excellent switching uniformity of Cu-Doped MoOx/GdOx bilayer for nonvolatile memory applications. IEEE Electron Device Lett 30(5):457–459. https://doi.org/10.1109/Led.2009.2015687

    Article  Google Scholar 

  40. Yu SM, Gao B, Fang Z, Yu HY, Kang JF, Wong HSP (2013) A low energy oxide-based electronic synaptic device for neuromorphic visual systems with tolerance to device variation. Adv Mater 25(12):1774–1779. https://doi.org/10.1002/adma.201203680

    Article  Google Scholar 

  41. Lee AR, Bae YC, Baek GH, Im HS, Hong JP (2013) Multi-level resistive switching observations in asymmetric Pt/Ta2O5 − x/TiOxNy/TiN/Ta2O5 − x/Pt multilayer configurations. Appl Phys Lett. https://doi.org/10.1063/1.4818129

    Google Scholar 

  42. Kumar D, Aluguri R, Chand U, Tseng TY (2017) Metal oxide resistive switching memory: materials, properties and switching mechanisms. Ceram Int 43:S547–S556. https://doi.org/10.1016/j.ceramint.2017.05.289

    Article  Google Scholar 

  43. Drachman DA (2005) Do we have brain to spare? Neurology 64(12):2004–2005. https://doi.org/10.1212/01.Wnl.0000166914.38327.Bb

    Article  Google Scholar 

  44. Suri M, Bichler O, Querlioz D, Palma G, Vianello E, Vuillaume D, Gamrat C, DeSalvo B (2012) CBRAM devices as binary synapses for low-power stochastic neuromorphic systems: auditory (Cochlea) and visual (Retina) cognitive processing applications. In: 2012 IEEE international electron devices meeting (IEDM)

  45. Choi H, Jung H, Lee J, Yoon J, Park J, Seong DJ, Lee W, Hasan M, Jung GY, Hwang H (2009) An electrically modifiable synapse array of resistive switching memory. Nanotechnology. https://doi.org/10.1088/0957-4484/20/34/345201

    Google Scholar 

  46. Seo K, Kim I, Jung S, Jo M, Park S, Park J, Shin J, Biju KP, Kong J, Lee K, Lee B, Hwang H (2011) Analog memory and spike-timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device. Nanotechnology. https://doi.org/10.1088/0957-4484/22/25/254023

    Google Scholar 

  47. Chang T, Jo SH, Lu W (2011) Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano 5(9):7669–7676. https://doi.org/10.1021/nn202983n

    Article  Google Scholar 

  48. Yang R, Terabe K, Liu GQ, Tsuruoka T, Hasegawa T, Gimzewski JK, Aono M (2012) On-demand nanodevice with electrical and neuromorphic multifunction realized by local ion migration. ACS Nano 6(11):9515–9521. https://doi.org/10.1021/nn302510e

    Article  Google Scholar 

  49. Wu Y, Yu SM, Wong HSP, Chen YS, Lee HY, Wang SM, Gu PY, Chen F, Tsai MJ (2012) AlOx-based resistive switching device with gradual resistance modulation for neuromorphic device application. In: IEEE international memory workshop

  50. Suri M, Bichler O, Hubert Q, Perniola L, Sousa V, Jahan C, Vuillaume D, Gamrat C, DeSalvo B (2012) Interface engineering of PCM for improved synaptic performance in neuromorphic systems. In: IEEE international memory workshop

  51. Eryilmaz SB, Kuzum D, Jeyasingh R, Kim S, BrightSky M, Lam C, Wong HSP (2014) Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front Neurosci-Switz. https://doi.org/10.3389/fnins.2014.00205

    Google Scholar 

  52. Garbin D, Suri M, Bichler O, Querlioz D, Gamrat C, DeSalvo B (2013) Probabilistic neuromorphic system using binary phase-change memory (PCM) synapses: detailed power consumption analysis. In: 2013 13th IEEE conference on nanotechnology (IEEE-Nano), pp 91–94

  53. Jo SH, Chang T, Ebong I, Bhadviya BB, Mazumder P, Lu W (2010) Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett 10(4):1297–1301. https://doi.org/10.1021/nl904092h

    Article  Google Scholar 

  54. Kang DH, Jun HG, Ryoo KC, Jeong H, Sohn H (2015) Emulation of spike-timing dependent plasticity in nano-scale phase change memory. Neurocomputing 155:153–158. https://doi.org/10.1016/j.neucom.2014.12.036

    Article  Google Scholar 

  55. Kim S, Ishii M, Lewis S, Perri T, BrightSky M, Kim W, Jordan R, Burr GW, Sosa N, Ray A, Han JP, Miller C, Hosokawa K, Lam C (2015) NVM neuromorphic core with 64 k-cell (256-by-256) phase change memory synaptic array with on-chip neuron circuits for continuous in situ learning. In: 2015 IEEE international electron devices meeting (IEDM)

  56. Shelby RM, Burr GW, Boybat I, Di Nolfo C (2015) Non-volatile memory as hardware synapse in neuromorphic computing: a first look at reliability issues. In: 2015 IEEE international reliability physics symposium (IRPS)

  57. Suri M, Bichler O, Querlioz D, Cueto O, Perniola L, Sousa V, Vuillaume D, Gamrat C, DeSalvo B (2011) Phase change memory as synapse for ultra-dense neuromorphic systems: application to complex visual pattern extraction. In: 2011 IEEE international electron devices meeting (IEDM)

  58. Suri M, Bichler O, Querlioz D, Traore B, Cueto O, Perniola L, Sousa V, Vuillaume D, Gamrat C, DeSalvo B (2012) Physical aspects of low power synapses based on phase change memory devices. J Appl Phys. https://doi.org/10.1063/1.4749411

    Google Scholar 

  59. Zhong YP, Li Y, Xu L, Miao XS (2015) Simple square pulses for implementing spike-timing-dependent plasticity in phase-change memory. Phys Status Solidi-R 9(7):414–419. https://doi.org/10.1002/pssr.201510150

    Article  Google Scholar 

  60. Kuzum D, Jeyasingh RGD, Lee B, Wong HSP (2012) Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett 12(5):2179–2186. https://doi.org/10.1021/nl201040y

    Article  Google Scholar 

  61. Kuzum D, Jeyasingh RGD, Wong HSP (2011) Energy efficient programming of nanoelectronic synaptic devices for large-scale implementation of associative and temporal sequence learning. In: 2011 IEEE international electron devices meeting (IEDM)

  62. Sharad M, Augustine C, Roy K (2012) Boolean and non-boolean computation with spin devices. In: 2012 IEEE international electron devices meeting (IEDM)

  63. Sangwan VK, Jariwala D, Kim IS, Chen KS, Marks TJ, Lauhon LJ, Hersam MC (2015) Gate-tunable memristive phenomena mediated by grain boundaries in single-layer MoS2. Nat Nanotechnol 10(5):403–406. https://doi.org/10.1038/Nnano.2015.56

    Article  Google Scholar 

  64. Sengupta A, Al Azim Z, Fong XY, Roy K (2015) Spin-orbit torque induced spike-timing dependent plasticity. Appl Phys Lett. https://doi.org/10.1063/1.4914111

    Google Scholar 

  65. Sengupta A, Parsa M, Han B, Roy K (2016) Probabilistic deep spiking neural systems enabled by magnetic tunnel junction. IEEE Trans Electron Dev 63(7):2963–2970. https://doi.org/10.1109/Ted.2016.2568762

    Article  Google Scholar 

  66. Sengupta A, Roy K (2016) Short-term plasticity and long-term potentiation in magnetic tunnel junctions: towards volatile synapses. Phys Rev Appl. https://doi.org/10.1103/PhysRevApplied.5.024012

    Google Scholar 

  67. Adachi M, Seki M, Yamahara H, Nasu H, Tabata H (2015) Long-term potentiation of magnonic synapses by photocontrolled spin current mimicked in reentrant spin-glass garnet ferrite Lu3Fe5-2xCoxSixO12 thin films. Appl Phys Express. https://doi.org/10.7567/Apex.8.043002

    Google Scholar 

  68. Roska T, Horvath A, Stubendek A, Corinto F, Csaba G, Porod W, Shibata T, Bourianoff G (2012) An associative memory with oscillatory CNN arrays using spin torque oscillator cells and spin-wave interactions architecture and end-to-end simulator. In: International work cell nano

  69. Fujita O, Amemiya Y (1993) A floating-gate analog memory device for neural networks. IEEE Trans Electron Dev 40(11):2029–2055. https://doi.org/10.1109/16.239745

    Article  Google Scholar 

  70. Hafliger P, Rasche C (1999) Floating gate analog memory for parameter and variable storage in a learning silicon neuron. In: ISCAS ‘99: proceedings of the 1999 IEEE international symposium on circuits and systems, vol 2, pp 416–419

  71. Harrison RR, Bragg JA, Hasler P, Minch BA, Deweerth SP (2001) A CMOS programmable analog memory-cell array using floating-gate circuits. IEEE Trans Circuits-II 48(1):4–11. https://doi.org/10.1109/82.913181

    Google Scholar 

  72. Brink S, Nease S, Hasler P (2013) Computing with networks of spiking neurons on a biophysically motivated floating-gate based neuromorphic integrated circuit. Neural Netw 45:39–49. https://doi.org/10.1016/j.neunet.2013.02.011

    Article  Google Scholar 

  73. Diorio C, Hasler P, Minch BA, Mead CA (1997) A floating-gate MOS learning array with locally computed weight updates. IEEE Trans Electron Dev 44(12):2281–2289. https://doi.org/10.1109/16.644652

    Article  Google Scholar 

  74. Diorio C, Hasler P, Minch BA, Mead C (1997) A complementary pair of four-terminal silicon synapses. Analog Integr Circ Signal Process 13(1–2):153–166. https://doi.org/10.1023/A:1008244314595

    Article  Google Scholar 

  75. Rahimi K, Diorio C, Hernandez C, Brockhausen MD (2002) A simulation model for floating-gate MOS synapse transistors. In: 2002 IEEE international symposium on circuits and systems, vol II, proceedings, pp 532–535

  76. Diorio C, Hasler P, Minch A, Mead CA (1996) A single-transistor silicon synapse. IEEE Trans Electron Dev 43(11):1972–1980. https://doi.org/10.1109/16.543035

    Article  Google Scholar 

  77. Cauwenberghs G, Neugebauer CF, Agranat AJ, Yariv A (1990) Large scale optoelectronic integration of asynchronous analog neural networks. In: International neural network conference: July 9–13, 1990 Palais Des Congres—Paris—France. Springer, Netherlands, pp 551–554. https://doi.org/10.1007/978-94-009-0643-3_1

  78. Burla M, Wang X, Li M, Chrostowski L, Azana J (2016) Wideband dynamic microwave frequency identification system using a low-power ultracompact silicon photonic chip. Nat Commun. https://doi.org/10.1038/ncomms13004

    Google Scholar 

  79. Rietman EA, Frye RC, Wong CC, Kornfeld CD (1989) Amorphous-silicon photoconductive arrays for artificial neural networks. Appl Opt 28(16):3474–3478

    Article  Google Scholar 

  80. Livingston DL, Albin S, Park SM (1991) A new method for storing weights in analog neural hardware. In: IEEE proceedings of the Southeastcon 91, vols 1 and 2, pp 86–88. https://doi.org/10.1109/secon.1991.147710

  81. Maier P, Hartmann F, Emmerling M, Schneider C, Kamp M, Hofling S, Worschech L (2016) Electro-photo-sensitive memristor for neuromorphic and arithmetic computing. Phys Rev Appl. https://doi.org/10.1103/PhysRevApplied.5.054011

    Google Scholar 

  82. Boukhobza J, Rubini S (2012) Flashing in the memory hierarchy: an overview on flash memory internals

  83. Kotecki DE (1997) A review of high dielectric materials for DRAM capacitors. Integr Ferroelectr 16(1–4):1–19. https://doi.org/10.1080/10584589708013025

    Article  Google Scholar 

  84. Pavan P, Bez R, Olivo P, Zanoni E (1997) Flash memory cells: an overview. Proc IEEE 85(8):1248–1271. https://doi.org/10.1109/5.622505

    Article  Google Scholar 

  85. Sethi P, Krishnia S, Gan WL, Kholid FN, Tan FN, Maddu R, Lew WS (2017) Bi-directional high speed domain wall motion in perpendicular magnetic anisotropy Co/Pt double stack structures. Sci Rep. https://doi.org/10.1038/s41598-017-05409-7

    Google Scholar 

  86. Zhang SF, Gan WL, Kwon J, Luo FL, Lim GJ, Wang JB, Lew WS (2016) Highly efficient domain walls injection in perpendicular magnetic anisotropy nanowire. Sci Rep. https://doi.org/10.1038/srep24804

    Google Scholar 

  87. Apalkov D, Dieny B, Slaughter JM (2016) Magnetoresistive random access memory. Proc IEEE 104(10):1796–1830. https://doi.org/10.1109/Jproc.2016.2590142

    Article  Google Scholar 

  88. Hsu J (2014) IBM’s new brain. IEEE Spectr 51(10):17–19

    Article  Google Scholar 

  89. Yu SM, Chen HY, Gao B, Kang JF, Wong HSP (2013) HfOx-based vertical resistive switching random access memory suitable for bit-cost-effective three-dimensional cross-point architecture. ACS Nano 7(3):2320–2325. https://doi.org/10.1021/nn305510u

    Article  Google Scholar 

  90. Zhang ZP, Wu Y, Wong HSP, Wong SS (2013) Nanometer-scale HfOx RRAM. IEEE Electron Dev Lett 34(8):1005–1007. https://doi.org/10.1109/Led.2013.2265404

    Article  Google Scholar 

  91. Yu SM, Gao B, Fang Z, Yu HY, Kang JF, Wong HSP (2013) Stochastic learning in oxide binary synaptic device for neuromorphic computing. Front Neurosci-Switz. https://doi.org/10.3389/fnins.2013.00186

    Google Scholar 

  92. Gao B, Bi YJ, Chen HY, Liu R, Huang P, Chen B, Liu LF, Liu XY, Yu SM, Wong HSP, Kang JF (2014) Ultra-low-energy three-dimensional oxide-based electronic synapses for implementation of robust high-accuracy neuromorphic computation systems. ACS Nano 8(7):6998–7004. https://doi.org/10.1021/nn501824r

    Article  Google Scholar 

  93. Liu JC, Hsu CW, Wang IT, Hou TH (2015) Categorization of multilevel-cell storage-class memory: an RRAM example. IEEE Trans Electron Dev 62(8):2510–2516. https://doi.org/10.1109/Ted.2015.2444663

    Article  Google Scholar 

  94. Hsu CW, Hou TH, Chen MC, Wang IT, Lo CL (2013) Bipolar Ni/TiO2/HfO2/Ni RRAM with multilevel states and self-rectifying characteristics. IEEE Electron Device Lett 34(7):885–887. https://doi.org/10.1109/Led.2013.2264823

    Article  Google Scholar 

  95. Hou TH (2015) 3D RRAM-based synaptic network with low programming energy. In: 5th stanford-IMEC International RRAM Workshop, Leuven, Belgium, pp 24–25

  96. Burr GW, Shelby RM, Sebastian A, Kim S, Kim S, Sidler S, Virwani K, Ishii M, Narayanan P, Fumarola A, Sanches LL, Boybat I, Le Gallo M, Moon K, Woo J, Hwang H, Leblebici Y (2017) Neuromorphic computing using non-volatile memory. Adv Phys-X 2(1):89–124. https://doi.org/10.1080/23746149.2016.1259585

    Google Scholar 

  97. Lee D, Park J, Moon K, Jang J-W, Park S, Chu M, Kim J, Noh J, Jeon M, Lee B, Lee B, Lee B-G, Hwang H (2015) Oxide based nanoscale analog synapse device for neural signal recognition system. In: Electron devices meeting (IEDM), 2015 IEEE international. https://doi.org/10.1109/iedm.2015.7409628

  98. Slutsky I, Abumaria N, Wu L-J, Huang C, Zhang L, Li B, Zhao X, Govindarajan A, Zhao M-G, Zhuo M, Tonegawa S, Liu G (2010) Enhancement of learning and memory by elevating brain magnesium. Neuron 65(2):165–177. https://doi.org/10.1016/j.neuron.2009.12.026

    Article  Google Scholar 

  99. Mayford M, Siegelbaum SA, Kandel ER (2012) Synapses and memory storage. Cold Spring Harb Perspect Biol 4(6):a005751. https://doi.org/10.1101/cshperspect.a005751

    Article  Google Scholar 

  100. Okano H, Hirano T, Balaban E (2000) Learning and memory. Proc Natl Acad Sci 97(23):12403–12404

    Article  Google Scholar 

  101. Shaw GL (1986) Donald Hebb: the organization of behavior. In: Palm G, Aertsen A (eds) Brain theory: proceedings of the first trieste meeting on brain theory, October 1–4, 1984. Springer, Berlin, pp 231–233. https://doi.org/10.1007/978-3-642-70911-1_15

  102. Bi GQ, Poo MM (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18(24):10464–10472

    Google Scholar 

  103. Zhang LI, Tao HW, Holt CE, Harris WA, Poo MM (1998) A critical window for cooperation and competition among developing retinotectal synapses. Nature 395(6697):37–44

    Article  Google Scholar 

  104. Markram H, Lubke J, Frotscher M, Sakmann B (1997) Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275(5297):213–215. https://doi.org/10.1126/science.275.5297.213

    Article  Google Scholar 

  105. Ohno T, Hasegawa T, Tsuruoka T, Terabe K, Gimzewski JK, Aono M (2011) Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat Mater 10(8):591–595. https://doi.org/10.1038/Nmat3054

    Article  Google Scholar 

  106. Chen XR, Feng J, Ma HL (2015) The resistive switching characteristics in tantalum oxide-based RRAM device via combining high-temperature sputtering with plasma oxidation. In: 2015 15th non-volatile memory technology symposium (NVMTS)

  107. Li YY, Liu ZT, Tan TT (2014) Resistive switching behavior of hafnium oxide thin film grown by magnetron sputtering. Rare Metal Mat Eng 43(1):24–27

    Article  Google Scholar 

  108. Wu Y, Lee B, Wong H-SP (2010) Ultra-low power Al2O3-based RRAM with 1 μA reset current. In: VLSI technology systems and applications (VLSI-TSA), 2010 international symposium on

  109. Chang KC, Tsai TM, Chang TC, Syu YE, Chuang SL, Li CH, Gan DS, Sze SM (2012) The effect of silicon oxide based RRAM with tin doping. Electrochem Solid-State Lett 15(3):H65–H68. https://doi.org/10.1149/2.013203esl

    Article  Google Scholar 

  110. Wang SY, Tsai CH, Lee DY, Lin CY, Lin CC, Tseng TY (2011) Improved resistive switching properties of Ti/ZrO2/Pt memory devices for RRAM application. Microelectron Eng 88(7):1628–1632. https://doi.org/10.1016/j.mee.2010.11.058

    Article  Google Scholar 

  111. Liu KC, Tzeng WH, Chang KM, Wu CH (2010) The effect of plasma deposition on the electrical characteristics of Pt/HfOx/TiN RRAM device. Surf Coat Technol 205:S379–S384. https://doi.org/10.1016/j.surfcoat.2010.08.043

    Article  Google Scholar 

  112. Simanjuntak FM, Panda D, Tsai T-L, Lin C-A, Wei K-H, Tseng T-Y (2015) Enhancing the memory window of AZO/ZnO/ITO transparent resistive switching devices by modulating the oxygen vacancy concentration of the top electrode. J Mater Sci 50(21):6961–6969. https://doi.org/10.1007/s10853-015-9247-y

    Article  Google Scholar 

  113. Wang J, Nonnenmann SS (2017) Area-dependent electroforming and switching polarity reversal across TiO2/Nb:SrTiO3 oxide interfaces. J Mater Sci 52(11):6469–6475. https://doi.org/10.1007/s10853-017-0882-3

    Article  Google Scholar 

  114. Wu Y, Lee B, Wong HSP (2010) Al2O3-Based RRAM using atomic layer deposition (ALD) with 1-mu a RESET current. IEEE Electron Device Lett 31(12):1449–1451. https://doi.org/10.1109/Led.2010.2074177

    Article  Google Scholar 

  115. Egorov KV, Lebedinskii YY, Markeev AM, Orlov OM (2015) Full ALD Ta2O5-based stacks for resistive random access memory grown with in vacuo XPS monitoring. Appl Surf Sci 356:454–459. https://doi.org/10.1016/j.apsusc.2015.07.217

    Article  Google Scholar 

  116. Sahu VK, Misra P, Ajimsha RS, Das AK, Joshi MP, Kukreja LM (2016) Resistive memory switching in ultrathin TiO2 films grown by atomic layer deposition. In: AIP conference proceedings vol 1731. https://doi.org/10.1063/1.4948104

  117. Niu G, Kim HD, Roelofs R, Perez E, Schubert MA, Zaumseil P, Costina I, Wenger C (2016) Material insights of HfO2-based integrated 1-transistor-1-resistor resistive random access memory devices processed by batch atomic layer deposition. Sci Rep. https://doi.org/10.1038/srep28155

    Google Scholar 

  118. Misra P, Das AK, Kukreja LM (2010) Switching characteristics of ZnO based transparent resistive random access memory devices grown by pulsed laser deposition. Phys Status Solidi C 7(6):1718–1720. https://doi.org/10.1002/pssc.200983244

    Article  Google Scholar 

  119. Wu Z, Zhu J (2017) Enhanced unipolar resistive switching characteristics of Hf0.5Zr0.5O2 thin films with high ON/OFF ratio. Materials. https://doi.org/10.3390/ma10030322

    Google Scholar 

  120. Parreira P, Paterson GW, McVitie S, MacLaren DA (2016) Stability, bistability and instability of amorphous ZrO2 resistive memory devices. J Phys D Appl Phys. https://doi.org/10.1088/0022-3727/49/9/095111

    Google Scholar 

  121. Xu ZD, Yu LN, Wu Y, Dong C, Deng N, Xu XG, Miao J, Jiang Y (2015) Low-energy resistive random access memory devices with no need for a compliance current. Sci Rep. https://doi.org/10.1038/srep10409

    Google Scholar 

  122. Hsu ST, Zhuang WW, Li TK, Pan W, Ignatiev A, Papagianni C, Wu NJ (2005) RRAM switching mechanism. In: 2005 Non-volatile memory technology symposium, proceedings, pp 121–124

  123. Preparation method of tungsten oxide variable-resistance material in resistive random access memory (2014) Google Patents

  124. Rodriguez R, Poulter B, Gonzalez M, Ali F, Lau LD, Mangun M (2017) The effect of tin on PECVD-deposited germanium sulfide thin films for resistive RAM devices. Mater Sci Appl 08:188–196

    Google Scholar 

  125. Yao J, Sun ZZ, Zhong L, Natelson D, Tour JM (2010) Resistive switches and memories from silicon oxide. Nano Lett 10(10):4105–4110. https://doi.org/10.1021/nl102255r

    Article  Google Scholar 

  126. Mustaqima M, Yoo P, Huang W, Lee BW, Liu CL (2015) Regulation of the forming process and the set voltage distribution of unipolar resistance switching in spin-coated CoFe2O4 thin films. Nanoscale Res Lett. https://doi.org/10.1186/s11671-015-0876-5

    Google Scholar 

  127. Xu HT, Wu CJ, Zhao XH, Jung RJ, Li Y, Liu CL (2017) Improved resistance switching stability in Fe-doped ZnO thin films through pulsed magnetic field annealing. Nanoscale Res Lett. https://doi.org/10.1186/s11671-017-1949-4

    Google Scholar 

  128. Zhang F, Zhuang WW, Evans DR, Hsu ST (2004) High temperature annealing of spin coated Pr1-xCaxMnO3 thim film for RRAM application. Google patents

  129. Chang YC, Wei CY, Wang YH (2012) Sol-gel barium titanate-based RRAM by inserting graphene oxide interlayer. 2012 IEEE international conference on electron devices and solid state circuit (EDSSC)

  130. Wang Y, Chen B, Liu D, Gao B, Liu LF, Liu X, Kang J (2014) Solution processed resistive random access memory devices for transparent solid-state circuit systems. In: MRS online proceedings library archive, vol 1633. https://doi.org/10.1557/opl.2014.252

  131. Lee C, Kim I, Choi W, Shin H, Cho J (2009) Resistive switching memory devices composed of binary transition metal oxides using sol–gel chemistry. Langmuir 25(8):4274–4278

    Article  Google Scholar 

  132. Kim HD, Yun MJ, Lee JH, Kim KH, Kim TG (2014) Transparent multi-level resistive switching phenomena observed in ITO/RGO/ITO memory cells by the sol-gel dip-coating method. Sci Rep. https://doi.org/10.1038/srep04614

    Google Scholar 

  133. Niu G, Calka P, Auf der Maur M, Santoni F, Guha S, Fraschke M, Hamoumou P, Gautier B, Perez E, Walczyk C, Wenger C, Di Carlo A, Alff L, Schroeder T (2016) Geometric conductive filament confinement by nanotips for resistive switching of HfO2-RRAM devices with high performance. Sci Rep 6:25757. https://doi.org/10.1038/srep25757

    Article  Google Scholar 

  134. Siddiqui G-U-D, Ali J, Doh Y-H, Choi KH (2016) Fabrication of zinc stannate based all-printed resistive switching device. Mater Lett 166(Supplement C):311–316. https://doi.org/10.1016/j.matlet.2015.12.045

    Article  Google Scholar 

  135. Asamitsu A, Tomioka Y, Kuwahara H, Tokura Y (1997) Current switching of resistive states in magnetoresistive manganites. Nature 388:50. https://doi.org/10.1038/40363

    Article  Google Scholar 

  136. Chen X, Wu NJ, Ignatiev A (2005) Perovskite RRAM devices with metal/insulator/PCMO/metal heterostructures. In: 2005 Non-volatile memory technology symposium, proceedings, pp 125–128

  137. Liu KC, Tzeng WH, Chang KM, Chan YC, Kuo CC, Cheng CW (2010) Transparent resistive random access memory (T-RRAM) based on Gd2O3 film and its resistive switching characteristics. In: INEC: 2010 3rd international nanoelectronics conference, vols 1 and 2, pp 898–899

  138. Park S, Kim H, Choo M, Noh J, Sheri A, Jung S, Seo K, Park J, Kim S, Lee W, Shin J, Lee D, Choi G, Woo J, Cha E, Jang J, Park C, Jeon M, Lee B, Lee BH, Hwang H (2012) RRAM-based synapse for neuromorphic system with pattern recognition function. In: 2012 IEEE international electron devices meeting (IEDM)

  139. Sangsu P, Jinwoo N, Myung-lae C, Ahmad Muqeem S, Man C, Young-Bae K, Chang Jung K, Moongu J, Byung-Geun L, Byoung Hun L, Hyunsang H (2013) Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device. Nanotechnology 24(38):384009

    Article  Google Scholar 

  140. Park S, Sheri A, Kim J, Noh J, Jang J, Jeon M, Lee B, Lee BR, Lee BH, Hwang H (2013) Neuromorphic speech systems using advanced ReRAM-based synapse. In: 2013 IEEE international electron devices meeting (IEDM)

  141. Sangsu P, Manzar S, Jinwoo N, Daeseuk L, Kibong M, Jiyong W, Byoung HL, Hyunsang H (2014) A nitrogen-treated memristive device for tunable electronic synapses. Semicond Sci Technol 29(10):104006

    Article  Google Scholar 

  142. Moon K, Park S, Jang J, Lee D, Woo J, Cha E, Lee S, Park J, Song J, Koo Y, Hwang H (2014) Hardware implementation of associative memory characteristics with analogue-type resistive-switching device. Nanotechnology. https://doi.org/10.1088/0957-4484/25/49/495204

    Google Scholar 

  143. Lee D, Moon K, Park J, Park S, Hwang H (2015) Trade-off between number of conductance states and variability of conductance change in Pr0.7Ca0.3MnO3-based synapse device. Appl Phys Lett. https://doi.org/10.1063/1.4915924

    Article  Google Scholar 

  144. Moon K, Cha E, Park J, Gi S, Chu M, Baek K, Lee B, Oh SH, Hwang H (2016) Analog synapse device with 5-b MLC and improved data retention for neuromorphic system. IEEE Electron Device Lett 37(8):1067–1070. https://doi.org/10.1109/Led.2016.2583545

    Article  Google Scholar 

  145. Moon K, Cha E, Lee D, Jang J, Park J, Hwang H (2016) ReRAM-based analog synapse and IMT neuron device for neuromorphic system. In: International symposium VLSI technology

  146. Yu S (2017) Neuro-inspired computing using resistive synaptic devices. Springer, Berlin

    Book  Google Scholar 

  147. Yu SM, Wu Y, Jeyasingh R, Kuzum DG, Wong HSP (2011) An electronic synapse device based on metal oxide resistive switching memory for neuromorphic computation. IEEE Trans Electron Dev 58(8):2729–2737. https://doi.org/10.1109/Ted.2011.2147791

    Article  Google Scholar 

  148. Gao B, Chen B, Zhang FF, Liu LF, Liu XY, Kang JF, Yu HY, Yu B (2013) A novel defect-engineering-based implementation for high-performance multilevel data storage in resistive switching memory. IEEE Trans Electron Dev 60(4):1379–1383. https://doi.org/10.1109/Ted.2013.2245508

    Article  Google Scholar 

  149. Garbin D, Vianello E, Bichler O, Rafhay Q, Gamrat C, Ghibaudo G, DeSalvo B, Perniola L (2015) HfO2-based OxRAM devices as synapses for convolutional neural networks. IEEE Trans Electron Dev 62(8):2494–2501. https://doi.org/10.1109/Ted.2015.2440102

    Article  Google Scholar 

  150. Benoist A, Blonkowski S, Jeannot S, Denorme S, Damiens J, Berger J, Candelier P, Vianello E, Grampeix H, Nodin JF, Jalaguier E, Perniola L, Allard B (2014) 28 nm Advanced CMOS resistive RAM solution as embedded non-volatile memory. In: International reliability physics symposium

  151. Guo T, Tan T, Liu Z (2015) The improved resistive switching of HfO2: Cu film with multilevel storage. J Mater Sci 50(21):7043–7047. https://doi.org/10.1007/s10853-015-9257-9

    Article  Google Scholar 

  152. Wang ZQ, Ambrogio S, Balatti S, Ielmini D (2015) A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems. Front Neurosci-Switz. https://doi.org/10.3389/fnins.2014.00438

    Google Scholar 

  153. Covi E, Brivio S, Fanciulli M, Spiga S (2015) Synaptic potentiation and depression in Al:HfO2-based memristor. Microelectron Eng 147:41–44. https://doi.org/10.1016/j.mee.2015.04.052

    Article  Google Scholar 

  154. Garbin D, Vianello E, Bichler O, Azzaz M, Rafhay Q, Candelier P, Gamrat C, Ghibaudo G, DeSalvo B, Pemiola L (2015) On the impact of OxRAM-based synapses variability on convolutional neural networks performance. In: IEEE international symposium nano, pp 193–198

  155. Lee MS, Lee JW, Kim CH, Park BG, Lee JH (2015) Implementation of short-term plasticity and long-term potentiation in a synapse using si-based type of charge-trap memory. IEEE Trans Electron Dev 62(2):569–573. https://doi.org/10.1109/Ted.2014.2378758

    Article  Google Scholar 

  156. Covi E, Brivio S, Serb A, Prodromakis T, Fanciulli M, Spiga S (2016) HfO2-based memristors for neuromorphic applications. IEEE International symposium on circuits systems, pp 393–396. https://doi.org/10.1109/iscas.2016.7527253

  157. Werner T, Garbin D, Vianello E, Bichler O, Cattaert D, Yvert B, De Salvo B, Perniola L (2016) Real-time decoding of brain activity by embedded spiking neural networks using OxRAM synapses. In: IEEE international symposium on circuits systems, pp 2318–2321

  158. Werner T, Vianello E, Bichler O, Garbin D, Cattaert D, Yvert B, De Salvo B, Perniola L (2017) Spiking neural networks based on OxRAM synapses for real-time unsupervised spike sorting (vol 10, pg474, 2016). Front Neurosci-Switz. https://doi.org/10.3389/fnins.2017.00486

    Google Scholar 

  159. Wang IT, Lin YC, Wang YF, Hsu CW, Hou TH (2014) 3D synaptic architecture with ultralow sub-10 fJ energy per spike for neuromorphic computation. In: 2014 IEEE international electron devices meeting (IEDM)

  160. Wang YF, Lin YC, Wang IT, Lin TP, Hou TH (2015) Characterization and modeling of nonfilamentary Ta/TaOx/TiO2/Ti analog synaptic device. Sci Rep. https://doi.org/10.1038/srep10150

    Google Scholar 

  161. Wang IT, Chih-Cheng C, Li-Wen C, Teyuh C, Tuo-Hung H (2016) 3D Ta/TaO x/TiO 2/Ti synaptic array and linearity tuning of weight update for hardware neural network applications. Nanotechnology 27(36):365204

    Article  Google Scholar 

  162. Gao LG, Wang IT, Chen PY, Vrudhula S, Seo JS, Cao Y, Hou TH, Yu SM (2015) Fully parallel write/read in resistive synaptic array for accelerating on-chip learning. Nanotechnology. https://doi.org/10.1088/0957-4484/26/45/455204

    Google Scholar 

  163. Wang ZW, Yin MH, Zhang T, Cai YM, Wang YY, Yang YC, Huang R (2016) Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing. Nanoscale 8(29):14015–14022. https://doi.org/10.1039/c6nr00476h

    Article  Google Scholar 

  164. Wang Q, He DY (2017) Time-decay memristive behavior and diffusive dynamics in one forget process operated by a 3D vertical Pt/Ta2O5-x/W device. Sci Rep. https://doi.org/10.1038/s41598-017-00985-0

    Google Scholar 

  165. Yao P, Wu HQ, Gao B, Eryilmaz SB, Huang XY, Zhang WQ, Zhang QT, Deng N, Shi LP, Wong HSP, Qian H (2017) Face classification using electronic synapses. Nat Commun. https://doi.org/10.1038/ncomms15199

    Google Scholar 

  166. Yu SM, Gao B, Fang Z, Yu HY, Kang JF, Wong HSP (2012) A neuromorphic visual system using RRAM synaptic devices with sub-pJ energy and tolerance to variability: experimental characterization and large-scale modeling. In: 2012 IEEE international electron devices meeting (IEDM)

  167. Berdan R, Vasilaki E, Khiat A, Indiveri G, Serb A, Prodromakis T (2016) Emulating short-term synaptic dynamics with memristive devices. Sci Rep. https://doi.org/10.1038/srep18639

    Google Scholar 

  168. Bousoulas P, Asenov P, Karageorgiou I, Sakellaropoulos D, Stathopoulos S, Tsoukalas D (2016) Engineering amorphous-crystalline interfaces in TiO2-x/TiO2-y-based bilayer structures for enhanced resistive switching and synaptic properties. J Appl Phys. https://doi.org/10.1063/1.4964872

    Google Scholar 

  169. Mostafa H, Khiat A, Serb A, Mayr CG, Indiveri G, Prodromakis T (2015) Implementation of a spike-based perceptron learning rule using TiO(2 − x) memristors. Front Neurosci 9:357. https://doi.org/10.3389/fnins.2015.00357

    Article  Google Scholar 

  170. Park J, Kwak M, Moon K, Woo J, Lee D, Hwang H (2016) TiOx-based RRAM synapse with 64-levels of conductance and symmetric conductance change by adopting a hybrid pulse scheme for neuromorphic computing. IEEE Electron Device Lett 37(12):1559–1562. https://doi.org/10.1109/Led.2016.2622716

    Article  Google Scholar 

  171. Akoh N, Asai T, Yanagida T, Kawai T, Amemiya Y (2010) A ReRAM-based analog synaptic device having spike-timing-dependent plasticity. IEICE Tech Rep 110(246):23–28

    Google Scholar 

  172. Hu SG, Liu Y, Chen TP, Liu Z, Yu Q, Deng LJ, Yin Y, Hosaka S (2013) Emulating the paired-pulse facilitation of a biological synapse with a NiOx-based memristor. Appl Phys Lett. https://doi.org/10.1063/1.4804374

    Google Scholar 

  173. Hu SG, Liu Y, Liu Z, Chen TP, Yu Q, Deng LJ, Yin Y, Hosaka S (2014) Synaptic long-term potentiation realized in Pavlov’s dog model based on a NiOx-based memristor. J Appl Phys. https://doi.org/10.1063/1.4902515

    Google Scholar 

  174. Chang T, Jo SH, Kim KH, Sheridan P, Gaba S, Lu W (2011) Synaptic behaviors and modeling of a metal oxide memristive device. Appl Phys A-Mater 102(4):857–863. https://doi.org/10.1007/s00339-011-6296-1

    Article  Google Scholar 

  175. Fan-Yi M, Shu-Kai D, Li-Dan W, Xiao-Fang H, Zhe-Kang D (2015) An improved WOx memristor model with synapse characteristic analysis. Acta Phys Sinica 64(14):148501

    Google Scholar 

  176. Yong Z, Yanling Y, Yuehua P, Weichang Z, Huajun Y, Zhu’ai Q, Binquan L, Yong Z, Dongsheng T (2014) Enhanced memristive performance of individual hexagonal tungsten trioxide nanowires by water adsorption based on Grotthuss mechanism. Mater Res Express 1(2):025025

    Article  Google Scholar 

  177. Prezioso M, Bayat FM, Hoskins B, Likharev K, Strukov D (2016) Self-adaptive spike-time-dependent plasticity of metal-oxide memristors. Sci Rep. https://doi.org/10.1038/srep21331

    Google Scholar 

  178. Banerjee W, Liu Q, Lv H, Long S, Liu M (2017) Electronic imitation of behavioral and psychological synaptic activities using TiOx/Al2O3-based memristor devices. Nanoscale 9(38):14442–14450. https://doi.org/10.1039/C7NR04741J

    Article  Google Scholar 

  179. Wang CH, He W, Tong Y, Zhao R (2016) Investigation and manipulation of different analog behaviors of memristor as electronic synapse for neuromorphic applications. Sci Rep. https://doi.org/10.1038/srep22970

    Google Scholar 

  180. Kim HJ, Zheng H, Park JS, Kim DH, Kang CJ, Jang JT, Kim DH, Yoon TS (2017) Artificial synaptic characteristics with strong analog memristive switching in a Pt/CeO2/Pt structure. Nanotechnology. https://doi.org/10.1088/1361-6528/aa712c

    Google Scholar 

  181. Guo LQ, Wan Q, Wan CJ, Zhu LQ, Shi Y (2013) Short-term memory to long-term memory transition mimicked in IZO homojunction synaptic transistors. IEEE Electron Device Lett 34(12):1581–1583. https://doi.org/10.1109/Led.2013.2286074

    Article  Google Scholar 

  182. Chang YF, Fowler B, Chen YC, Zhou F, Pan CH, Chang TC, Lee JC (2016) Demonstration of synaptic behaviors and resistive switching characterizations by proton exchange reactions in silicon oxide. Sci Rep. https://doi.org/10.1038/srep21268

    Google Scholar 

  183. Driscoll T, Palit S, Qazilbash MM, Brehm M, Keilmann F, Chae B-G, Yun S-J, Kim H-T, Cho SY, Jokerst NM, Smith DR, Basov DN (2008) Dynamic tuning of an infrared hybrid-metamaterial resonance using vanadium dioxide. Appl Phys Lett 93(2):024101. https://doi.org/10.1063/1.2956675

    Article  Google Scholar 

  184. Driscoll T, Kim H-T, Chae B-G, Kim B-J, Lee Y-W, Jokerst NM, Palit S, Smith DR, Di Ventra M, Basov DN (2009) memory metamaterials. Science 325(5947):1518–1521. https://doi.org/10.1126/science.1176580

    Article  Google Scholar 

  185. Lee MJ, Park Y, Suh DS, Lee EH, Seo S, Kim DC, Jung R, Kang BS, Ahn SE, Lee CB, Seo DH, Cha YK, Yoo IK, Kim JS, Park BH (2007) Two series oxide resistors applicable to high speed and high density nonvolatile memory. Adv Mater 19(22):3919–3923. https://doi.org/10.1002/adma.200700251

    Article  Google Scholar 

  186. Son M, Lee J, Park J, Shin J, Choi G, Jung S, Lee W, Kim S, Park S, Hwang H (2011) Excellent selector characteristics of nanoscale VO2 for high-density bipolar ReRAM applications. IEEE Electron Device Lett 32(11):1579–1581. https://doi.org/10.1109/LED.2011.2163697

    Article  Google Scholar 

  187. Zhang K, Wang B, Wang F, Han Y, Jian X, Zhang H, Wong HSP (2016) VO2-based selection device for passive resistive random access memory application. IEEE Electron Device Lett 37(8):978–981. https://doi.org/10.1109/LED.2016.2582259

    Google Scholar 

  188. Krzysteczko P, Munchenberger J, Schafers M, Reiss G, Thomas A (2012) The memristive magnetic tunnel junction as a nanoscopic synapse-neuron system. Adv Mater 24(6):762–766. https://doi.org/10.1002/adma.201103723

    Article  Google Scholar 

  189. Yoshida C, Tsunoda K, Noshiro H, Sugiyama Y (2007) High speed resistive switching in Pt/TiO2/TiN film for nonvolatile memory application. Appl Phys Lett 91(22):223510. https://doi.org/10.1063/1.2818691

    Article  Google Scholar 

  190. Yoshida C, Kinoshita K, Yamasaki T, Sugiyama Y (2008) Direct observation of oxygen movement during resistance switching in NiO/Pt film. Appl Phys Lett 93(4):042106. https://doi.org/10.1063/1.2966141

    Article  Google Scholar 

  191. Huang Y-C, Chen P-Y, Chin T-S, Liu R-S, Huang C-Y, Lai C-H (2012) Improvement of resistive switching in NiO-based nanowires by inserting Pt layers. Appl Phys Lett 101(15):153106. https://doi.org/10.1063/1.4758482

    Article  Google Scholar 

  192. Chien WC, Lee MH, Feng-Ming L, Lin YY, Hsiang-Lan L, Hsieh KY, Chih-Yuan L (2011) Multi-level 40 nm WOX resistive memory with excellent reliability. In: 2011 International electron devices meeting, 5–7 Dec. 2011 2011. pp 313531–313534. https://doi.org/10.1109/iedm.2011.6131651

  193. Chao X, Biao N, Long Z, Long-Hui Z, Yong-Qiang Y, Xian-He W, Qun-Ling F, Lin-Bao L, Yu-Cheng W (2013) High-performance nonvolatile Al/AlO x/CdTe: Sb nanowire memory device. Nanotechnology 24(35):355203

    Article  Google Scholar 

  194. Chih Yang Lina CYW, Wua Chung Yi, Hub Chenming, Tseng Tseung Yuen (2007) Bistable resistive switching in Al2O3 memory thin films. J Electrochem Soc. https://doi.org/10.1149/1.2750450

    Google Scholar 

  195. Zhu W, Chen TP, Yang M, Liu Y, Fung S (2012) Resistive Switching Behavior of Partially Anodized Aluminum Thin Film at Elevated Temperatures. IEEE Trans Electron Devices. https://doi.org/10.1109/TED.2012.2205692

    Google Scholar 

  196. Chen YS, Chen B, Gao B, Zhang FF, Qiu YJ, Lian GJ, Liu LF, Liu XY, Han RQ, Kang JF (2010) Anticrosstalk characteristics correlated with the set process for α-Fe2O3/Nb–SrTiO3 stack-based resistive switching device. Appl Phys Lett 97(26):262112. https://doi.org/10.1063/1.3532970

    Article  Google Scholar 

  197. Muraoka S, Osano K, Kanzawa Y, Mitani S, Fujii S, Katayama K, Katoh Y, Wei Z, Mikawa T, Arita K, Kawashima Y, Azuma R, Kawai K, Shimakawa K, Odagawa A, Takagi T (2007) Fast switching and long retention Fe-O ReRAM and its switching mechanism. In: 2007 IEEE international electron devices meeting, 10–12 Dec. 2007. pp 779–782. https://doi.org/10.1109/iedm.2007.4419063

  198. Dou C, Kakushima K, Ahmet P, Tsutsui K, Nishiyama A, Sugii N, Natori K, Hattori T, Iwai H (2012) Resistive switching behavior of a CeO2 based ReRAM cell incorporated with Si buffer layer. Microelectron Reliab 52(4):688–691. https://doi.org/10.1016/j.microrel.2011.10.019

    Article  Google Scholar 

  199. Lee D, D-j Seong, Jo I, Xiang F, Dong R, Oh S, Hwang H (2007) Resistance switching of copper doped MoOx films for nonvolatile memory applications. Appl Phys Lett 90(12):122104. https://doi.org/10.1063/1.2715002

    Article  Google Scholar 

  200. Li Y, Sinitskii A, Tour JM (2008) Electronic two-terminal bistable graphitic memories. Nat Mater 7:966. https://doi.org/10.1038/nmat2331

    Article  Google Scholar 

  201. Syu YE, Zhang R, Chang TC, Tsai TM, Chang KC, Lou JC, Young TF, Chen JH, Chen MC, Yang YL, Shih CC, Chu TJ, Chen JY, Pan CH, Su YT, Huang HC, Gan DS, Sze SM (2013) Endurance improvement technology with nitrogen implanted in the interface of WSiOx resistance switching device. IEEE Electron Device Lett 34(7):864–866. https://doi.org/10.1109/LED.2013.2260125

    Article  Google Scholar 

  202. Yu D, Liu LF, Chen B, Zhang FF, Gao B, Fu YH, Liu XY, Kang JF, Zhang X (2011) Multilevel resistive switching characteristics in Ag/SiO2/Pt RRAM devices. In: 2011 IEEE international conference of electron devices and solid-state circuits, 17–18 Nov. 2011. pp 1–2. https://doi.org/10.1109/edssc.2011.6117721

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Acknowledgements

This work was supported by a RIE2020 AME-Programmatic Grant (Neuromorphic computing, No. A1687b0033) and an Industry-IHL Partnership Program (NRF2015-IIP001-001). WSL is a member of the Singapore Spintronics Consortium (SG-SPIN).

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Authors and Affiliations

  1. Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore, 637371, Singapore

    XiaoLiang Hong, Desmond JiaJun Loy, Putu Andhita Dananjaya, Funan Tan & WenSiang Lew

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 21 Nanyang Link, Singapore, 637371, Singapore

    CheeMang Ng

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  1. XiaoLiang Hong
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  2. Desmond JiaJun Loy
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Correspondence to WenSiang Lew.

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Hong, X., Loy, D.J., Dananjaya, P.A. et al. Oxide-based RRAM materials for neuromorphic computing. J Mater Sci 53, 8720–8746 (2018). https://doi.org/10.1007/s10853-018-2134-6

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  • DOI: https://doi.org/10.1007/s10853-018-2134-6

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