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P. A. Dananjaya et al.
44. Y. Lin et al., Transferable and flexible artificial memristive synapse based on WO x schottky
junction on arbitrary substrates. Adv. Electron. Mater. 4(12), 1800373 (2018)
45. T. Chang, S.-H. Jo, W. Lu, Short-term memory to long-term memory transition in a nanoscale
memristor. ACS Nano 5(9), 7669–7676, 2011/09/27 (2011)
46. T. Chang, S.-H. Jo, K.-H. Kim, P. Sheridan, S. Gaba, W. Lu, Synaptic behaviors and modeling
of a metal oxide memristive device. Appl. Phys. A 102(4), 857–863, 2011/03/01 (2011)
47. S. Jabeen, M. Ismail, A. M. Rana, E. Ahmed, Impact of work function on the resistive switching
characteristics of M/ZnO/CeO2/Pt devices. Mater. Res. Expr. 4(5), 056401, 2017/05/16 (2017)
48. U. Russo, D. Kamalanathan, D. Ielmini, A.L. Lacaita, M.N. Kozicki, Study of multilevel
programming in programmable metallization cell (PMC) memory. IEEE Trans. Electr. Dev.
56(5), 1040–1047 (2009)
49. K. Aratani et al., A novel resistance memory with high scalability and nanosecond switching,
in 2007 IEEE International Electron Devices Meeting pp. 783–786 (2007)
50. M. Kund et al., Conductive bridging RAM (CBRAM): an emerging non-volatile memory
technology scalable to sub 20 nm, in IEEE International Electron Devices Meeting, 2005.
IEDM Technical Digest. pp. 754–757 (2005)
51. S. Sills et al., A copper ReRAM cell for storage class memory applications, in 2014 Symposium
on VLSI Technology (VLSI-Technology): Digest of Technical Papers, pp. 1–2 (2014)
52. S. Yasuda et al., A cross point Cu-ReRAM with a novel OTS selector for storage class memory
applications, in 2017 Symposium on VLSI Technology, pp. T30–T31 (2017)
53. J. Guy et al., Investigation of the physical mechanisms governing data-retention in down
to 10 nm nano-trench Al2O3/CuTeGe conductive bridge RAM (CBRAM), in 2013 IEEE
International Electron Devices Meeting, pp. 30.2.1–30.2.4 (2013)
54. S. Fujii et al., Scaling the CBRAM switching layer diameter to 30 nm improves cycling
endurance. IEEE Electr. Dev. Lett. 39(1), 23–26 (2018)
55. S. Z. Rahaman et al., Excellent resistive memory characteristics and switching mechanism
using a Ti nanolayer at the Cu/TaO x interface, (in eng). Nanoscale Res. Lett. 7(1), 345–345
(2012)
56. A. Belmonte et al., 90nm W\Al 2 O 3 \TiW\Cu 1T1R CBRAM cell showing low-power, fast and
disturb-free operation, in 2013 5th IEEE International Memory Workshop, pp. 26–29 (2013)
57. S. Z. Rahaman et al., Impact of TaO x nanolayer at the GeSe x /W interface on resistive switching
memory performance and investigation of Cu nanofilament. J. Appl.Phys. 111(6), 063710
(2012)
58. E.O. Neftci, B.U. Pedroni, S. Joshi, M. Al-Shedivat, G. Cauwenberghs, Stochastic synapses
enable efficient brain-inspired learning machines. Front. Neurosci. 10, 241 (2016)
59. J.H. Lee, K.K. Likharev, Defect-tolerant nanoelectronic pattern classifiers. Int. J. Circ. Theory
Appl. 35(3), 239–264 (2007)
60. M. Suri et al., Bio-inspired stochastic computing using binary CBRAM synapses. IEEE Trans.
Electr. Dev. 60(7), 2402–2409 (2013)
61. S.H. Jo, T. Chang, I. Ebong, B.B. Bhadviya, P. Mazumder, W. Lu, Nanoscale memristor device
as synapse in neuromorphic systems. Nano Lett. 10(4), 1297–1301, 2010/04/14 (2010)
62. X. Yan et al., Memristor with Ag-cluster-doped TiO 2 films as artificial synapse for
neuroinspired computing. Adv. Func. Mater. 28(1), 1705320 (2018)
63. T.D. Dongale, S.V. Mohite, A.A. Bagade, R.K. Kamat, K.Y. Rajpure, Bio-mimicking the
synaptic weights, analog memory, and forgetting effect using spray deposited WO3 memristor
device. Microelectr. Eng. 183–184, 12–18, 2017/11/05 (2017)
64. J.H. Yoon et al., Truly electroforming-free and low-energy memristors with preconditioned
conductive tunneling paths. Adv. Func. Mater. 27(35), 1702010 (2017)
65. Y. Wang et al., Self-doping memristors with equivalently synaptic ion dynamics for
neuromorphic computing. ACS Appl. Mater. Interf. 11(27), 24230–24240, 2019/07/10 (2019)
66. Y. Cao, Y. Chen, D. Khosla, Spiking deep convolutional neural networks for energy-efficient
object recognition. Int. J. Comput. Vis. 113(1), 54–66, 2015/05/01 (2015)
67. I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, Y. Bengio, “Binarized neural networks,
Presented at the Proceedings of the 30th International Conference on Neural Information
Processing Systems, Barcelona, Spain (2016)
P. A. Dananjaya et al.
44. Y. Lin et al., Transferable and flexible artificial memristive synapse based on WO x schottky
junction on arbitrary substrates. Adv. Electron. Mater. 4(12), 1800373 (2018)
45. T. Chang, S.-H. Jo, W. Lu, Short-term memory to long-term memory transition in a nanoscale
memristor. ACS Nano 5(9), 7669–7676, 2011/09/27 (2011)
46. T. Chang, S.-H. Jo, K.-H. Kim, P. Sheridan, S. Gaba, W. Lu, Synaptic behaviors and modeling
of a metal oxide memristive device. Appl. Phys. A 102(4), 857–863, 2011/03/01 (2011)
47. S. Jabeen, M. Ismail, A. M. Rana, E. Ahmed, Impact of work function on the resistive switching
characteristics of M/ZnO/CeO2/Pt devices. Mater. Res. Expr. 4(5), 056401, 2017/05/16 (2017)
48. U. Russo, D. Kamalanathan, D. Ielmini, A.L. Lacaita, M.N. Kozicki, Study of multilevel
programming in programmable metallization cell (PMC) memory. IEEE Trans. Electr. Dev.
56(5), 1040–1047 (2009)
49. K. Aratani et al., A novel resistance memory with high scalability and nanosecond switching,
in 2007 IEEE International Electron Devices Meeting pp. 783–786 (2007)
50. M. Kund et al., Conductive bridging RAM (CBRAM): an emerging non-volatile memory
technology scalable to sub 20 nm, in IEEE International Electron Devices Meeting, 2005.
IEDM Technical Digest. pp. 754–757 (2005)
51. S. Sills et al., A copper ReRAM cell for storage class memory applications, in 2014 Symposium
on VLSI Technology (VLSI-Technology): Digest of Technical Papers, pp. 1–2 (2014)
52. S. Yasuda et al., A cross point Cu-ReRAM with a novel OTS selector for storage class memory
applications, in 2017 Symposium on VLSI Technology, pp. T30–T31 (2017)
53. J. Guy et al., Investigation of the physical mechanisms governing data-retention in down
to 10 nm nano-trench Al2O3/CuTeGe conductive bridge RAM (CBRAM), in 2013 IEEE
International Electron Devices Meeting, pp. 30.2.1–30.2.4 (2013)
54. S. Fujii et al., Scaling the CBRAM switching layer diameter to 30 nm improves cycling
endurance. IEEE Electr. Dev. Lett. 39(1), 23–26 (2018)
55. S. Z. Rahaman et al., Excellent resistive memory characteristics and switching mechanism
using a Ti nanolayer at the Cu/TaO x interface, (in eng). Nanoscale Res. Lett. 7(1), 345–345
(2012)
56. A. Belmonte et al., 90nm W\Al 2 O 3 \TiW\Cu 1T1R CBRAM cell showing low-power, fast and
disturb-free operation, in 2013 5th IEEE International Memory Workshop, pp. 26–29 (2013)
57. S. Z. Rahaman et al., Impact of TaO x nanolayer at the GeSe x /W interface on resistive switching
memory performance and investigation of Cu nanofilament. J. Appl.Phys. 111(6), 063710
(2012)
58. E.O. Neftci, B.U. Pedroni, S. Joshi, M. Al-Shedivat, G. Cauwenberghs, Stochastic synapses
enable efficient brain-inspired learning machines. Front. Neurosci. 10, 241 (2016)
59. J.H. Lee, K.K. Likharev, Defect-tolerant nanoelectronic pattern classifiers. Int. J. Circ. Theory
Appl. 35(3), 239–264 (2007)
60. M. Suri et al., Bio-inspired stochastic computing using binary CBRAM synapses. IEEE Trans.
Electr. Dev. 60(7), 2402–2409 (2013)
61. S.H. Jo, T. Chang, I. Ebong, B.B. Bhadviya, P. Mazumder, W. Lu, Nanoscale memristor device
as synapse in neuromorphic systems. Nano Lett. 10(4), 1297–1301, 2010/04/14 (2010)
62. X. Yan et al., Memristor with Ag-cluster-doped TiO 2 films as artificial synapse for
neuroinspired computing. Adv. Func. Mater. 28(1), 1705320 (2018)
63. T.D. Dongale, S.V. Mohite, A.A. Bagade, R.K. Kamat, K.Y. Rajpure, Bio-mimicking the
synaptic weights, analog memory, and forgetting effect using spray deposited WO3 memristor
device. Microelectr. Eng. 183–184, 12–18, 2017/11/05 (2017)
64. J.H. Yoon et al., Truly electroforming-free and low-energy memristors with preconditioned
conductive tunneling paths. Adv. Func. Mater. 27(35), 1702010 (2017)
65. Y. Wang et al., Self-doping memristors with equivalently synaptic ion dynamics for
neuromorphic computing. ACS Appl. Mater. Interf. 11(27), 24230–24240, 2019/07/10 (2019)
66. Y. Cao, Y. Chen, D. Khosla, Spiking deep convolutional neural networks for energy-efficient
object recognition. Int. J. Comput. Vis. 113(1), 54–66, 2015/05/01 (2015)
67. I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, Y. Bengio, “Binarized neural networks,
Presented at the Proceedings of the 30th International Conference on Neural Information
Processing Systems, Barcelona, Spain (2016)