Oxide-based RRAM materials for neuromorphic computing
Source abstract
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.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Transition Metal Oxide Nanomaterials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
Show source-provided references
1. Hickmott TW (1962) Low-frequency negative resistance in thin anodic oxide films. J Appl Phys 33(9):2669–2682. https://doi.org/10.1063/1.170253010.1063/1.1702530 ↗
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/10.1063/1.12646410.1063/1.126464 ↗
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/srep0533310.1038/srep05333 ↗
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.2007.441906110.1109/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/nature0693210.1038/nature06932 ↗
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):140–15310.1109/JSSC.2013.2280296 ↗
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/C6NR02029A10.1039/C6NR02029A ↗
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.2015.11.00910.1016/j.mattod.2015.11.009 ↗
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/srep1068410.1038/srep10684 ↗
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.2012.219036910.1109/JPROC.2012.2190369 ↗
15. Kuzum D, Yu SM, Wong HSP (2013) Synaptic electronics: materials, devices and applications. Nanotechnology. https://doi.org/10.1088/0957-4484/24/38/38200110.1088/0957-4484/24/38/382001 ↗
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.2010.207083010.1109/JPROC.2010.2070830 ↗
17. Waser R, Aono M (2007) Nanoionics-based resistive switching memories. Nat Mater 6(11):833–840. https://doi.org/10.1038/nmat202310.1038/nmat2023 ↗
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.2016.1310.1109/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/c6tc03447k10.1039/C6TC03447K ↗
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.2007.441906010.1109/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.2012.218277010.1109/TED.2012.2182770 ↗
22. Magyari-Kope B, Duncan D, Zhao L, Nishi Y (2016) Doping technology for RRAM—opportunities and challenges. In: International symposium on VLSI technology10.1109/VLSI-TSA.2016.7480494 ↗
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)10.1109/IEDM.2012.6479084 ↗
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.20070120510.1002/pssr.200701205 ↗
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/10.1088/1674-4926/37/6/06400110.1088/1674-4926/37/6/064001 ↗
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/10.1088/1674-1056/23/11/11730510.1088/1674-1056/23/11/117305 ↗
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.2013.225131410.1109/LED.2013.2251314 ↗
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/10.1063/1.281869110.1063/1.2818691 ↗
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/10.1088/0268-1242/23/7/07501910.1088/0268-1242/23/7/075019 ↗
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/10.1088/0957-4484/23/32/32570210.1088/0957-4484/23/32/325702 ↗
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/s10853-016-0326-510.1007/s10853-016-0326-5 ↗
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/s10853-016-9753-610.1007/s10853-016-9753-6 ↗
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/nn401212p10.1021/nn401212p ↗
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 electron10.1109/CDE.2017.7905205 ↗
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)10.1109/IEDM.2014.7046988 ↗
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.2010.209582010.1109/LED.2010.2095820 ↗
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.2009.201568710.1109/LED.2009.2015687 ↗
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.20120368010.1002/adma.201203680 ↗
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/10.1063/1.481812910.1063/1.4818129 ↗
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.2017.05.28910.1016/j.ceramint.2017.05.289 ↗
43. Drachman DA (2005) Do we have brain to spare? Neurology 64(12):2004–2005. https://doi.org/10.1212/01.Wnl.0000166914.38327.Bb10.1212/01.WNL.0000166914.38327.BB ↗
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)10.1109/IEDM.2012.6479017 ↗
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/10.1088/0957-4484/20/34/34520110.1088/0957-4484/20/34/345201 ↗
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/10.1088/0957-4484/22/25/25402310.1088/0957-4484/22/25/254023 ↗
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/nn202983n10.1021/nn202983n ↗
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/nn302510e10.1021/nn302510e ↗
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 workshop10.1109/IMW.2012.6213663 ↗
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 workshop10.1109/IMW.2012.6213674 ↗
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.2014.0020510.3389/fnins.2014.00205 ↗
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 91–9410.1109/NANO.2013.6721057 ↗
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/nl904092h10.1021/nl904092h ↗
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.2014.12.03610.1016/j.neucom.2014.12.036 ↗
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)10.1109/IEDM.2015.7409716 ↗
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)10.1109/IRPS.2015.7112755 ↗
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)10.1109/IEDM.2011.6131488 ↗
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/10.1063/1.474941110.1063/1.4749411 ↗
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.20151015010.1002/pssr.201510150 ↗
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/nl201040y10.1021/nl201040y ↗
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)10.1109/IEDM.2011.6131643 ↗
62. Sharad M, Augustine C, Roy K (2012) Boolean and non-boolean computation with spin devices. In: 2012 IEEE international electron devices meeting (IEDM)10.1109/IEDM.2012.6479026 ↗
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.2015.5610.1038/nnano.2015.56 ↗
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/10.1063/1.491411110.1063/1.4914111 ↗
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.2016.256876210.1109/TED.2016.2568762 ↗
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.5.02401210.1103/PhysRevApplied.5.024012 ↗
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.8.04300210.7567/Apex.8.043002 ↗
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 nano10.1109/CNNA.2012.6331463 ↗
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/10.1109/16.23974510.1109/16.239745 ↗
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 416–41910.1109/ISCAS.1999.780749 ↗
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/10.1109/82.91318110.1109/82.913181 ↗
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.2013.02.01110.1016/j.neunet.2013.02.011 ↗
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/10.1109/16.64465210.1109/16.644652 ↗
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:100824431459510.1023/A:1008244314595 ↗
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 532–53510.1109/ISCAS.2002.1011042 ↗
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/10.1109/16.54303510.1109/16.543035 ↗
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_110.1007/978-94-009-0643-3_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/ncomms1300410.1038/ncomms13004 ↗
79. Rietman EA, Frye RC, Wong CC, Kornfeld CD (1989) Amorphous-silicon photoconductive arrays for artificial neural networks. Appl Opt 28(16):3474–347810.1364/AO.28.003474 ↗
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.1991.14771010.1109/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.5.05401110.1103/PhysRevApplied.5.054011 ↗
82. Boukhobza J, Rubini S (2012) Flashing in the memory hierarchy: an overview on flash memory internals10.4018/978-1-4666-3934-8.ch015 ↗
83. Kotecki DE (1997) A review of high dielectric materials for DRAM capacitors. Integr Ferroelectr 16(1–4):1–19. https://doi.org/10.1080/1058458970801302510.1080/10584589708013025 ↗
84. Pavan P, Bez R, Olivo P, Zanoni E (1997) Flash memory cells: an overview. Proc IEEE 85(8):1248–1271. https://doi.org/10.1109/5.62250510.1109/5.622505 ↗
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/s41598-017-05409-710.1038/s41598-017-05409-7 ↗
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/srep2480410.1038/srep24804 ↗
87. Apalkov D, Dieny B, Slaughter JM (2016) Magnetoresistive random access memory. Proc IEEE 104(10):1796–1830. https://doi.org/10.1109/Jproc.2016.259014210.1109/JPROC.2016.2590142 ↗
88. Hsu J (2014) IBM’s new brain. IEEE Spectr 51(10):17–1910.1109/MSPEC.2014.6905473 ↗
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/nn305510u10.1021/nn305510u ↗
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.2013.226540410.1109/LED.2013.2265404 ↗
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.2013.0018610.3389/fnins.2013.00186 ↗
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/nn501824r10.1021/nn501824r ↗
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.2015.244466310.1109/TED.2015.2446956 ↗
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.2013.226482310.1109/LED.2013.2264823 ↗
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/10.1080/23746149.2016.125958510.1080/23746149.2016.1259585 ↗
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.2015.740962810.1109/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.2009.12.02610.1016/j.neuron.2009.12.026 ↗
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.a00575110.1101/cshperspect.a005751 ↗
100. Okano H, Hirano T, Balaban E (2000) Learning and memory. Proc Natl Acad Sci 97(23):12403–1240410.1073/pnas.210381897 ↗