Review Article
Issue
Published:
13 April 2021
Memory-centric neuromorphic computing for unstructured data processing
Keon Jae Lee(
)
)
Keywords:
memristor, neuromorphic computing, memory-centric, artificial synapses, artificial neurons, memristive neural network
Topics:
Nanoelectronics
Cite this article:
Sung SH, Kim TJ, Shin H, et al.
Memory-centric neuromorphic computing for unstructured data processing.
Nano Research,
2021, 14(9): 3126-3142.
https://doi.org/10.1007/s12274-021-3452-6
Download citation
821
Views
43
Downloads
Citations
21
Crossref
19
WoS
20
Scopus
0
CSCD
The unstructured data such as visual information, natural language, and human behaviors opens up a wide array of opportunities in the field of artificial intelligence (AI). The memory-centric neuromorphic computing (MNC) has been proposed for the efficient processing of unstructured data, bypassing the von Neumann bottleneck of current computing architecture. The development of MNC would provide massively parallel processing of unstructured data, realizing the cognitive AI in edge and wearable systems. In this review, recent advances in memory-centric neuromorphic devices are discussed in terms of emerging nonvolatile memories, volatile switches, synaptic plasticity, neuronal models, and memristive neural network.
menu
Abstract
Full text
Outline
About this article
Memory-centric neuromorphic computing for unstructured data processing
Keon Jae Lee(
)
)
Abstract
The unstructured data such as visual information, natural language, and human behaviors opens up a wide array of opportunities in the field of artificial intelligence (AI). The memory-centric neuromorphic computing (MNC) has been proposed for the efficient processing of unstructured data, bypassing the von Neumann bottleneck of current computing architecture. The development of MNC would provide massively parallel processing of unstructured data, realizing the cognitive AI in edge and wearable systems. In this review, recent advances in memory-centric neuromorphic devices are discussed in terms of emerging nonvolatile memories, volatile switches, synaptic plasticity, neuronal models, and memristive neural network.
Keywords:
memristor, neuromorphic computing, memory-centric, artificial synapses, artificial neurons, memristive neural network
References(212)
[1]
Keum, D. H.; Kim, S. K.; Koo, J.; Lee, G. H.; Jeon, C.; Mok, J. W.; Mun, B. H.; Lee, K. J.; Kamrani, E.; Joo, C. K. et al. Wireless smart contact lens for diabetic diagnosis and therapy. Sci. Adv. 2020, 6, eaba3252.
[2]
Azad, P.; Navimipour, N. J.; Rahmani, A. M.; Sharifi, A. The role of structured and unstructured data managing mechanisms in the Internet of things. Cluster Comput. 2020, 23, 1185-1198.
[3]
Chaudhari, N.; Srivastava, S. Big data security issues and challenges. In 2016 IEEE International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India, 2016, pp 60-64.
[4]
Paschen, U.; Pitt, C.; Kietzmann, J. Artificial intelligence: Building blocks and an innovation typology. Bus. Horiz. 2020, 63, 147-155.
[5]
Jung, Y. H.; Hong, S. K.; Wang, H. S.; Han, J. H.; Pham, T. X.; Park, H.; Kim, J.; Kang, S.; Yoo, C. D.; Lee, K. J. Flexible piezoelectric acoustic sensors and machine learning for speech processing. Adv. Mater. 2020, 32, 1904020.
[6]
Park, D. Y.; Joe, D. J.; Kim, D. H.; Park, H.; Han, J. H.; Jeong, C. K.; Park, H.; Park, J. G.; Joung, B.; Lee, K. J. Self-powered real-time arterial pulse monitoring using ultrathin epidermal piezoelectric sensors. Adv. Mater. 2017, 29, 1702308.
[7]
Hwang, G. T.; Byun, M.; Jeong, C. K.; Lee, K. J. Flexible piezoelectric thin-film energy harvesters and nanosensors for biomedical applications. Adv. Healthc. Mater. 2015, 4, 646-658.
[8]
Lee, H. E.; Lee, D.; Lee, T. I.; Shin, J. H.; Choi, G. M.; Kim, C.; Lee, S. H.; Lee, J. H.; Kim, Y. H.; Kang, S. M. et al. Wireless powered wearable micro light-emitting diodes. Nano Energy 2019, 55, 454-462.
[9]
Lee, H. E.; Shin, J. H.; Lee, S. H.; Lee, J. H.; Park, S. H.; Lee, K. J. Flexible micro light-emitting diodes for wearable applications. In Proceedings Volume 10940, Light-Emitting Devices, Materials, and Applications, San Francisco, California, USA, 2019, p 109400F.
[10]
Hwang, G. T.; Annapureddy, V.; Han, J. H.; Joe, D. J.; Baek, C.; Park, D. Y.; Kim, D. H.; Park, J. H.; Jeong, C. K.; Park, K. I. et al. Self-powered wireless sensor node enabled by an aerosol-deposited PZT flexible energy harvester. Adv. Energy Mater. 2016, 6, 1600237.
[11]
Khan, M. B.; Kim, D. H.; Han, J. H.; Saif, H.; Lee, H.; Lee, Y.; Kim, M.; Jang, E.; Hong, S. K.; Joe, D. J. et al. Performance improvement of flexible piezoelectric energy harvester for irregular human motion with energy extraction enhancement circuit. Nano Energy 2019, 58, 211-219.
[12]
Lee, I. Big data: Dimensions, evolution, impacts, and challenges. Bus. Horiz. 2017, 60, 293-303.
[13]
Wu, J.; Li, H.; Lin, Z. X.; Goh, K. Y. How big data and analytics reshape the wearable device market—The context of e-health. Int. J. Prod. Res. 2017, 55, 5168-5182.
[14]
Russom, P. BI Search and Text Analytics: New Additions to the BI Technology Stack[Online]. Media, Inc., 2007. https://tdwi.org/research/2007/04/bpr-2q-bi-search-and-text-analytics.aspx?tc=page0&tc=assetpg (accessed Dec 21, 2020).
[15]
Rao, R. From unstructured data to actionable intelligence. IT Prof. 2003, 5, 29-35.
[16]
Tanwar, M.; Duggal, R.; Khatri, S. K. Unravelling unstructured data: A wealth of information in big data. In 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), Noida, India, 2015, pp 1-6.
[17]
Han, J. H.; Bae, K. M.; Hong, S. K.; Park, H.; Kwak, J. H.; Wang, H. S.; Joe, D. J.; Park, J. H.; Jung, Y. H.; Hur, S. et al. Machine learning-based self-powered acoustic sensor for speaker recognition. Nano Energy 2018, 53, 658-665.
[18]
Lee, H. S.; Chung, J.; Hwang, G. T.; Jeong, C. K.; Jung, Y.; Kwak, J. H.; Kang, H.; Byun, M.; Kim, W. D.; Hur, S. et al. Flexible inorganic piezoelectric acoustic nanosensors for biomimetic artificial hair cells. Adv. Funct. Mater. 2014, 24, 6914-6921.
[19]
Han, J. H.; Kwak, J. H.; Joe, D. J.; Hong, S. K.; Wang, H. S.; Park, J. H.; Hur, S.; Lee, K. J. Basilar membrane-inspired self-powered acoustic sensor enabled by highly sensitive multi tunable frequency band. Nano Energy 2018, 53, 198-205.
[20]
Akopyan, F.; Sawada, J.; Cassidy, A.; Alvarez-Icaza, R.; Arthur, J.; Merolla, P.; Imam, N.; Nakamura, Y.; Datta, P.; Nam, G. J. et al. TrueNorth: Design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2015, 34, 1537-1557.
[21]
Merolla, P. A.; Arthur, J. V.; Alvarez-Icaza, R.; Cassidy, A. S.; Sawada, J.; Akopyan, F.; Jackson, B. L.; Imam, N.; Guo, C.; Nakamura, Y. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 2014, 345, 668-673.
[22]
Davies, M.; Srinivasa, N.; Lin, T. H.; Chinya, G.; Cao, Y. Q.; Choday, S. H.; Dimou, G.; Joshi, P.; Imam, N.; Jain, S. et al. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 2018, 38, 82-99.
[23]
Imam, N.; Cleland, T. A. Rapid online learning and robust recall in a neuromorphic olfactory circuit. Nat. Mach. Intell. 2020, 2, 181-191.
[24]
Backus, J. Can programming be liberated from the von Neumann style?: A functional style and its algebra of programs. Commun. ACM 1978, 21, 613-641.
[25]
Zidan, M. A.; Strachan, J. P.; Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 2018, 1, 22-29.
[26]
Li, Y. B.; Wang, Z. R.; Midya, R.; Xia, Q. F.; Yang, J. J. Review of memristor devices in neuromorphic computing: Materials sciences and device challenges. J. Phys. D Appl. Phys. 2018, 51, 503002.
[27]
Hu, M.; Li, H.; Chen, Y. R.; Wu, Q.; Rose, G. S.; Linderman, R. W. Memristor crossbar-based neuromorphic computing system: A case study. IEEE Trans. Neural Netw. Learn. Syst. 2014, 25, 1864-1878.
[28]
Zhang, X. J.; Huang, A. P.; Hu, Q.; Xiao, Z. S.; Chu, P. K. Neuromorphic computing with memristor crossbar. Phys. Status Solidi A 2018, 215, 1700875.
[29]
Cai, F. X.; Correll, J. M.; Lee, S. H.; Lim, Y.; Bothra, V.; Zhang, Z. Y.; Flynn, M. P.; Lu, W. D. A fully integrated reprogrammable memristor-CMOS system for efficient multiply-accumulate operations. Nat. Electron. 2019, 2, 290-299.
[30]
Moore, S. K. First Programmable Memristor Computer[Online]. IEEE Spectrum, 2019. https://spectrum.ieee.org/tech-talk/semiconductors/processors/first-programmable-memristor-computer (accessed Dec 21, 2020).
[31]
Li, Y. S.; Ang, K. W. Hardware implementation of neuromorphic computing using large-scale memristor crossbar arrays. Adv. Intell. Syst. 2021, 3, 2000137.
[32]
Yang, R.; Huang, H. M.; Guo, X. Memristive synapses and neurons for bioinspired computing. Adv. Electron. Mater. 2019, 5, 1900287.
[33]
Ebong, I. E.; Mazumder, P. CMOS and memristor-based neural network design for position detection. Proc. IEEE 2012, 100, 2050-2060.
[34]
Zucker, R. S. Calcium- and activity-dependent synaptic plasticity. Curr. Opin. Neurobiol. 1999, 9, 305-313.
[35]
Gerrow, K.; Triller, A. Synaptic stability and plasticity in a floating world. Curr. Opin. Neurobiol. 2010, 20, 631-639.
[36]
Wang, J. R.; Zhuge, F. Memristive synapses for brain-inspired computing. Adv. Mater. Technol. 2019, 4, 1800544.
[37]
Yan, X. B.; Zhao, J. H.; Liu, S.; Zhou, Z. Y.; Liu, Q.; Chen, J. S.; Liu, X. Y. Memristor with Ag-cluster-doped TiO2 films as artificial synapse for neuroinspired computing. Adv. Funct. Mater. 2018, 28, 1705320.
[38]
Lee, T. H.; Hwang, H. G.; Woo, J. U.; Kim, D. H.; Kim, T. W.; Nahm, S. Synaptic plasticity and metaplasticity of biological synapse realized in a KNbO3 memristor for application to artificial synapse. ACS Appl. Mater. Interfaces 2018, 10, 25673-25682.
[39]
Yan, X. B.; Li, X. Y.; Zhou, Z. Y.; Zhao, J. H.; Wang, H.; Wang, J. J.; Zhang, L.; Ren, D. L.; Zhang, X.; Chen, J. S. et al. Flexible transparent organic artificial synapse based on the tungsten/egg albumen/indium tin oxide/polyethylene terephthalate memristor. ACS Appl. Mater. Interfaces 2019, 11, 18654-18661.
[40]
La Barbera, S.; Ly, D. R. B.; Navarro, G.; Castellani, N.; Cueto, O.; Bourgeois, G.; De Salvo, B.; Nowak, E.; Querlioz, D.; Vianello, E. Narrow heater bottom electrode-based phase change memory as a bidirectional artificial synapse. Adv. Electron. Mater. 2018, 4, 1800223.
[41]
Majumdar, S.; Tan, H. W.; Qin, Q. H.; van Dijken, S. Energy-efficient organic ferroelectric tunnel junction memristors for neuromorphic computing. Adv. Electron. Mater. 2019, 5, 1800795.
[42]
Zayer, F.; Dghais, W.; Benabdeladhim, M.; Hamdi, B. Low power, ultrafast synaptic plasticity in 1R-ferroelectric tunnel memristive structure for spiking neural networks. AEU Int. J. Electron. Commun. 2019, 100, 56-65.
[43]
Abuwasib, M.; Lee, H.; Sharma, P.; Eom, C. B.; Gruverman, A.; Singisetti, U. CMOS compatible integrated ferroelectric tunnel junctions (FTJ). In 2015 73rd Annual Device Research Conference (DRC), Columbus, OH, USA, 2015, pp 45-46.
[44]
Zhang, S. R.; Zhou, L.; Mao, J. Y.; Ren, Y.; Yang, J. Q.; Yang, G. H.; Zhu, X.; Han, S. T.; Roy, V. A. L.; Zhou, Y. Artificial synapse emulated by charge trapping-based resistive switching device. Adv. Mater. Technol. 2019, 4, 1800342.
[45]
Tian, B. B.; Liu, L.; Yan, M. G.; Wang, J. L.; Zhao, Q. B.; Zhong, N.; Xiang, P. H.; Sun, L.; Peng, H.; Shen, H. et al. A robust artificial synapse based on organic ferroelectric polymer. Adv. Electron. Mater. 2019, 5, 1800600.
[46]
Hodgkin, A. L.; Huxley, A. F. A quantitative description of membrane current and its application to conduction and excitation in nerve. Bull. Math. Biol. 1990, 52, 25-71.
[47]
Abbott, L. F. Lapicque's introduction of the integrate-and-fire model neuron (1907). Brain Res. Bull. 1999, 50, 303-304.
[48]
Zhang, X. M.; Wang, W.; Liu, Q.; Zhao, X. L.; Wei, J. S.; Cao, R. R.; Yao, Z. H.; Zhu, X. L.; Zhang, F.; Lv, H. B. et al. An artificial neuron based on a threshold switching memristor. IEEE Electron Device Lett. 2018, 39, 308-311.
[49]
Kalita, H.; Krishnaprasad, A.; Choudhary, N.; Das, S.; Dev, D.; Ding, Y.; Tetard, L.; Chung, H. S.; Jung, Y.; Roy, T. Artificial neuron using vertical MoS2/graphene threshold switching memristors. Sci. Rep. 2019, 9, 53.
[50]
Lin, J.; Annadi, A.; Sonde, S.; Chen, C.; Stan, L.; Achari, K. V. L. V.; Ramanathan, S.; Guha, S. Low-voltage artificial neuron using feedback engineered insulator-to-metal-transition devices. In IEEE International Electron Devices Meeting, San Francisco, CA, USA, 2016, pp 34.5.1-34.5.4.
[51]
Lee, M.; Cho, S. W.; Kim, S. J.; Kwak, J. Y.; Ju, H.; Yi, Y.; Cheong, B. K.; Lee, S. Simple artificial neuron using an ovonic threshold switch featuring spike-frequency adaptation and chaotic activity. Phys. Rev. Appl. 2020, 13, 064056.
[52]
Bao, L.; Kang, J.; Fang, Y. C.; Yu, Z. Z.; Wang, Z. W.; Yang, Y. C.; Cai, Y. M.; Huang, R. Artificial shape perception retina network based on tunable memristive neurons. Sci. Rep. 2018, 8, 13727.
[53]
Bo, Y. H.; Zhang, P.; Zhang, Y. W.; Song, J.; Li, S.; Liu, X. J. Spiking dynamic behaviors of NbO2 memristive neurons: A model study. J. Appl. Phys. 2020, 127, 245101.
[54]
Kim, D. H.; Yoo, H. G.; Hong, S. M.; Jang, B.; Park, D. Y.; Joe, D. J.; Kim, J. H.; Lee, K. J. Simultaneous roll transfer and interconnection of flexible silicon NAND flash memory. Adv. Mater. 2016, 28, 8371-8378.
[55]
Krestinskaya, O.; James, A. P.; Chua, L. O. Neuromemristive circuits for edge computing: A review. IEEE Trans. Neural Netw. Learn. Syst. 2020, 31, 4-23.
[56]
Zhou, F. C.; Zhou, Z.; Chen, J. W.; Choy, T. H.; Wang, J. L.; Zhang, N.; Lin, Z. Y.; Yu, S. M.; Kang, J. F.; Wong, H. S. P. et al. Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat. Nanotechnol. 2019, 14, 776-782.
[57]
Chang, T. C.; Chang, K. C.; Tsai, T. M.; Chu, T. J.; Sze, S. M. Resistance random access memory. Mater. Today 2016, 19, 254-264.
[58]
Banerjee, W. Challenges and applications of emerging nonvolatile memory devices. Electronics 2020, 9, 1029.
[59]
Zahoor, F.; Zulkifli, T. Z. A.; Khanday, F. A. Resistive random access memory (RRAM): An overview of materials, switching mechanism, performance, multilevel cell (mlc) storage, modeling, and applications. Nanoscale Res. Lett. 2020, 15, 90.
[60]
Fantini, P. Phase change memory applications: The history, the present and the future. J. Phys. D Appl. Phys. 2020, 53, 283002.
[61]
Lee, H. E.; Park, J. H.; Kim, T. J.; Im, D.; Shin, J. H.; Kim, D. H.; Mohammad, B.; Kang, I. S.; Lee, K. J. Novel electronics for flexible and neuromorphic computing. Adv. Funct. Mater. 2018, 28, 1801690.
[62]
Sung, S. H.; Kim, D. H.; Kim, T. J.; Kang, I. S.; Lee, K. J. Unconventional inorganic-based memristive devices for advanced intelligent systems. Adv. Mater. Technol. 2019, 4, 1900080.
[63]
Chen, Y. R. Reshaping future computing systems with emerging nonvolatile memory technologies. IEEE Micro 2019, 39, 54-57.
[64]
Le Gallo, M.; Sebastian, A. An overview of phase-change memory device physics. J. Phys. D Appl. Phys. 2020, 53, 213002.
[65]
Zhang, Z. H.; Wang, Z. W.; Shi, T.; Bi, C.; Rao, F.; Cai, Y. M.; Liu, Q.; Wu, H. Q.; Zhou, P. Memory materials and devices: From concept to application. InfoMat 2020, 2, 261-290.
[66]
Zhao, Q. L.; Xie, Z. J.; Peng, Y. P.; Wang, K. Y.; Wang, H. D.; Li, X. N.; Wang, H. W.; Chen, J. S.; Zhang, H.; Yan, X. B. Current status and prospects of memristors based on novel 2D materials. Mater. Horiz. 2020, 7, 1495-1518.
[67]
Khalid, M. Review on various memristor models, characteristics, potential applications, and future works. Trans. Electr. Electron. Mater. 2019, 20, 289-298.
[68]
Perez, T.; Calazans, N. L. V.; De Rose, C. A. F. A preliminary study on system-level impact of persistent main memory. In 13th International Symposium on Quality Electronic Design (ISQED), Santa Clara, CA, USA, 2012, pp 84-90.
[69]
Park, J. H.; Han, S.; Kim, D.; You, B. K.; Joe, D. J.; Hong, S.; Seo, J.; Kwon, J.; Jeong, C. K.; Park, H. J. et al. Plasmonic-tuned flash cu nanowelding with ultrafast photochemical-reducing and interlocking on flexible plastics. Adv. Funct. Mater. 2017, 27, 1701138.
[70]
Atabaki, A. H.; Moazeni, S.; Pavanello, F.; Gevorgyan, H.; Notaros, J.; Alloatti, L.; Wade, M. T.; Sun, C.; Kruger, S. A.; Meng, H. Y. et al. Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip. Nature 2018, 556, 349-354.
[71]
Sangwan, V. K.; Hersam, M. C. Neuromorphic nanoelectronic materials. Nat. Nanotechnol. 2020, 15, 517-528.
[72]
Zhang, L.; Gong, T.; Wang, H. D.; Guo, Z. N.; Zhang, H. Memristive devices based on emerging two-dimensional materials beyond graphene. Nanoscale 2019, 11, 12413-12435.
[73]
Pi, S.; Li, C.; Jiang, H.; Xia, W. W.; Xin, H. L.; Yang, J. J.; Xia, Q. F. Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension. Nat. Nanotechnol. 2019, 14, 35-39.
[74]
Wang, C.; Wu, H. Q.; Gao, B.; Wu, W.; Dai, L. J.; Li, X. Y.; Qian, H. Ultrafast RESET analysis of HfOx-based RRAM by sub-nanosecond pulses. Adv. Electron. Mater. 2017, 3, 1700263.
[75]
Shi, K. X.; Xu, H. Y.; Wang, Z. Q.; Zhao, X. N.; Liu, W. Z.; Ma, J. G.; Liu, Y. C. Improved performance of Ta2O5-x resistive switching memory by Gd-doping: Ultralow power operation, good data retention, and multilevel storage. Appl. Phys. Lett. 2017, 111, 223505.
[76]
Wu, X.; Yu, K. H.; Cha, D. K.; Bosman, M.; Raghavan, N.; Zhang, X. X.; Li, K.; Liu, Q.; Sun, L. T.; Pey, K. Atomic scale modulation of self-rectifying resistive switching by interfacial defects. Adv. Sci. 2018, 5, 1800096.
[77]
Al-Haddad, A.; Wang, C. L.; Qi, H. Y.; Grote, F.; Wen, L. Y.; Bernhard, J.; Vellacheri, R.; Tarish, S.; Nabi, G.; Kaiser, U. et al. Highly-ordered 3D vertical resistive switching memory arrays with ultralow power consumption and ultrahigh density. ACS Appl. Mater. Interfaces 2016, 8, 23348-23355.
[78]
Bagdzevicius, S.; Maas, K.; Boudard, M.; Burriel, M. Interface-type resistive switching in perovskite materials. J. Electroceram. 2017, 39, 157-184.
[79]
Fu, J. B.; Hua, M. X.; Ding, S. L.; Chen, X. G.; Wu, R.; Liu, S. Q.; Han, J. Z.; Wang, C. S.; Du, H. L.; Yang, Y. C. et al. Stability and its mechanism in Ag/CoOx/Ag interface-type resistive switching device. Sci. Rep. 2016, 6, 35630.
[80]
Bagdzevicius, S.; Boudard, M.; Caicedo, J. M.; Rapenne, L.; Mescot, X.; Rodríguez-Lamas, R.; Robaut, F.; Santiso, J.; Burriel, M. Superposition of interface and volume type resistive switching in perovskite nanoionic devices. J. Mater. Chem. C 2019, 7, 7580-7592.
[81]
Fan, Z.; Fan, H.; Yang, L.; Li, P. L.; Lu, Z. X.; Tian, G.; Huang, Z. F.; Li, Z. W.; Yao, J. X.; Luo, Q. Y. et al. Resistive switching induced by charge trapping/detrapping: A unified mechanism for colossal electroresistance in certain Nb:SrTiO3-based heterojunctions. J. Mater. Chem. C 2017, 5, 7317-7327.
[82]
Sun, W.; Gao, B.; Chi, M. F.; Xia, Q. F.; Yang, J. J.; Qian, H.; Wu, H. Q. Understanding memristive switching via in situ characterization and device modeling. Nat. Commun. 2019, 10, 3453.
[83]
Ielmini, D. Resistive switching memories based on metal oxides: Mechanisms, reliability and scaling. Semicond. Sci. Technol. 2016, 31, 063002.
[84]
Yoo, H. G.; Kim, S.; Lee, K. J. Flexible one diode-one resistor resistive switching memory arrays on plastic substrates. RSC Adv. 2014, 4, 20017-20023.
[85]
Jin, H. M.; Park, D. Y.; Jeong, S. J.; Lee, G. Y.; Kim, J. Y.; Mun, J. H.; Cha, S. K.; Lim, J.; Kim, J. S.; Kim, K. H. et al. Flash light millisecond self-assembly of high χ block copolymers for wafer-scale sub-10 nm nanopatterning. Adv. Mater. 2017, 29, 1700595.
[86]
Niu, G.; Calka, P.; der Maur, M. A.; Santoni, F.; Guha, S.; Fraschke, M.; Hamoumou, P.; Gautier, B.; Perez, E.; Walczyk, C. et al. Geometric conductive filament confinement by nanotips for resistive switching of HfO2-RRAM devices with high performance. Sci. Rep. 2016, 6, 25757.
[87]
Akbari, M.; Kim, M. K.; Kim, D.; Lee, J. S. Reproducible and reliable resistive switching behaviors of AlOX/HfOX bilayer structures with Al electrode by atomic layer deposition. RSC Adv. 2017, 7, 16704-16708.
[88]
Nallagatla, V. R.; Jo, J.; Acharya, S. K.; Kim, M.; Jung, C. U. Confining vertical conducting filament for reliable resistive switching by using a Au-probe tip as the top electrode for epitaxial brownmillerite oxide memristive device. Sci. Rep. 2019, 9, 1188.
[89]
Xiao, W.; Song, W. D.; Feng, Y. P.; Gao, D. Q.; Zhu, Y.; Ding, J. Electrode-controlled confinement of conductive filaments in a nanocolumn embedded symmetric-asymmetric RRAM structure. J. Mater. Chem. C 2020, 8, 1577-1582.
[90]
Li, Y.; Long, S. B.; Liu, Q.; Lv, H. B.; Liu, M. Resistive switching performance improvement via modulating nanoscale conductive filament, involving the application of two-dimensional layered materials. Small 2017, 13, 1604306.
[91]
Kim, S. M.; Kim, H. J.; Jung, H. J.; Kim, S. H.; Park, J. Y.; Seok, T. J.; Park, T. J.; Lee, S. W. Highly Uniform resistive switching performances using two-dimensional electron gas at a thin-film heterostructure for conductive bridge random access memory. ACS Appl. Mater. Interfaces 2019, 11, 30028-30036.
[92]
You, B. K.; Kim, J. M.; Joe, D. J.; Yang, K.; Shin, Y.; Jung, Y. S.; Lee, K. J. Reliable memristive switching memory devices enabled by densely packed silver nanocone arrays as electric-field concentrators. ACS Nano 2016, 10, 9478-9488.
[93]
Mun, B. H.; You, B. K.; Yang, S. R.; Yoo, H. G.; Kim, J. M.; Park, W. I.; Yin, Y.; Byun, M.; Jung, Y. S.; Lee, K. J. Flexible one diode-one phase change memory array enabled by block copolymer self-assembly. ACS Nano 2015, 9, 4120-4128.
[94]
Loke, D. K.; Skelton, J. M.; Lee, T. H.; Zhao, R.; Chong, T. C.; Elliott, S. R. Ultrafast nanoscale phase-change memory enabled by single-pulse conditioning. ACS Appl. Mater. Interfaces 2018, 10, 41855-41860.
[95]
Guo, T. Q.; Song, S. N.; Li, L.; Ji, X. L.; Li, C.; Xu, C.; Shen, L. L.; Xue, Y.; Liu, B.; Song, Z. T. et al. The ultrafast phase-change memory with high-thermal stability based on SiC-doped antimony. Scr. Mater. 2017, 129, 56-60.
[96]
Wang, W. J.; Shi, L. P.; Zhao, R.; Lim, K. G.; Lee, H. K.; Chong, T. C.; Wu, Y. H. Fast phase transitions induced by picosecond electrical pulses on phase change memory cells. Appl. Phys. Lett. 2008, 93, 043121.
[97]
Park, W. I.; You, B. K.; Mun, B. H.; Seo, H. K.; Lee, J. Y.; Hosaka, S.; Yin, Y.; Ross, C. A.; Lee, K. J.; Jung, Y. S. Self-assembled incorporation of modulated block copolymer nanostructures in phase-change memory for switching power reduction. ACS Nano 2013, 7, 2651-2658.
[98]
Kim, D. H.; Lee, H. E.; You, B. K.; Cho, S. B.; Mishra, R.; Kang, I. S.; Lee, K. J. Flexible crossbar-structured phase change memory array via mo-based interfacial physical lift-off. Adv. Funct. Mater. 2019, 29, 1806338.
[99]
Park, J. H.; Kim, S. W.; Kim, J. H.; Ko, D. H.; Wu, Z.; Ahn, J. K.; Ahn, D. H.; Lee, J. M.; Kang, S. B.; Choi, S. Y. Phase change memory employing a Ti diffusion barrier for reducing reset current. Thin Solid Films 2016, 612, 135-140.
[100]
Aryana, K.; Gaskins, J. T.; Nag, J.; Stewart, D. A.; Bai, Z. Q.; Mukhopadhyay, S.; Read, J. C.; Olson, D. H.; Hoglund, E. R.; Howe, J. M. et al. Interface controlled thermal resistances of ultra-thin chalcogenide-based phase change memory devices. Nat. Commun. 2021, 12, 774.
[101]
Wu, L.; Chen, Y. F.; Cai, D. L.; Lu, Y. Y.; Guo, T. Q.; Liu, Y. G.; Chen, X.; Zhang, S. F.; Yan, S.; Li, Y. et al. RESET current optimization for phase change memory based on the sub-threshold slope. Mater. Sci. Semicon. Proc. 2019, 97, 11-16.
[102]
You, B. K.; Byun, M.; Kim, S.; Lee, K. J. Self-structured conductive filament nanoheater for chalcogenide phase transition. ACS Nano 2015, 9, 6587-6594.
[103]
Nukala, P.; Lin, C. C.; Composto, R.; Agarwal, R. Ultralow-power switching via defect engineering in germanium telluride phase-change memory devices. Nat. Commun. 2016, 7, 10482.
[104]
Wen, Z.; Wu, D. Ferroelectric tunnel junctions: Modulations on the potential barrier. Adv. Mater. 2020, 32, 1904123.
[105]
Velev, J. P.; Burton, J. D.; Zhuravlev, M. Y.; Tsymbal, E. Y. Predictive modelling of ferroelectric tunnel junctions. npj Comput. Mater. 2016, 2, 16009.
[106]
Li, J. K.; Ge, C.; Du, J. Y.; Wang, C.; Yang, G. Z.; Jin, K. J. Reproducible ultrathin ferroelectric domain switching for high-performance neuromorphic computing. Adv. Mater. 2020, 32, 1905764.
[107]
Chen, A. P.; Zhang, W. R.; Dedon, L. R.; Chen, D.; Khatkhatay, F.; MacManus-Driscoll, J. L.; Wang, H. Y.; Yarotski, D.; Chen, J. et al. Couplings of polarization with interfacial deep trap and Schottky interface controlled ferroelectric memristive switching. Adv. Funct. Mater. 2020, 30, 2000664.
[108]
Wu, J. B.; Chen, H. Y.; Yang, N.; Cao, J.; Yan, X. D.; Liu, F. X.; Sun, Q. B.; Ling, X.; Guo, J.; Wang, H. High tunnelling electroresistance in a ferroelectric van der Waals heterojunction via giant barrier height modulation. Nat. Electron. 2020, 3, 466-472.
[109]
Ma, C.; Luo, Z.; Huang, W. C.; Zhao, L. T.; Chen, Q. L.; Lin, Y.; Liu, X.; Chen, Z. W.; Liu, C. C.; Sun, H. Y. et al. Sub-nanosecond memristor based on ferroelectric tunnel junction. Nat. Commun. 2020, 11, 1439.
[110]
Fan, Z.; Chen, J. S.; Wang, J. Ferroelectric HfO2-based materials for next-generation ferroelectric memories. J. Adv. Dielectr. 2016, 6, 1630003.
[111]
Richter, C.; Schenk, T.; Park, M. H.; Tscharntke, F. A.; Grimley, E. D.; LeBeau, J. M.; Zhou, C. Z.; Fancher, C. M.; Jones, J. L.; Mikolajick, T. et al. Si doped hafnium oxide—A “fragile” ferroelectric system. Adv. Electron. Mater. 2017, 3, 1700131.
[112]
Ryu, H.; Wu, H. N.; Rao, F. B.; Zhu, W. J. Ferroelectric tunneling junctions based on aluminum oxide/zirconium-doped hafnium oxide for neuromorphic computing. Sci. Rep. 2019, 9, 20383.
[113]
Kobayashi, M.; Tagawa, Y.; Mo, F.; Saraya, T.; Hiramoto, T. Ferroelectric HfO2 tunnel junction memory with high TER and multi-level operation featuring metal replacement process. IEEE J. Electron Devices Soc. 2019, 7, 134-139.
[114]
Lee, H. J.; Lee, M.; Lee, K.; Jo, J.; Yang, H.; Kim, Y.; Chae, S. C.; Waghmare, U.; Lee, J. H. Scale-free ferroelectricity induced by flat phonon bands in HfO2. Science 2020, 369, 1343-1347.
[115]
Goh, Y.; Jeon, S. Enhanced tunneling electroresistance effects in HfZrO-based ferroelectric tunnel junctions by high-pressure nitrogen annealing. Appl. Phys. Lett. 2018, 113, 052905.
[116]
Chen, L.; Wang, T. Y.; Dai, Y. W.; Cha, M. Y.; Zhu, H.; Sun, Q. Q.; Ding, S. J.; Zhou, P.; Chua, L.; Zhang, D. W. Ultra-low power Hf0.5Zr0.5O2 based ferroelectric tunnel junction synapses for hardware neural network applications. Nanoscale 2018, 10, 15826-15833.
[117]
Yoo, H. K.; Kim, J. S.; Zhu, Z.; Choi, Y. S.; Yoon, A.; MacDonald, M. R.; Lei, X.; Lee, T. Y.; Lee, D.; Chae, S. C. et al. Engineering of ferroelectric switching speed in Si doped HfO2 for high-speed 1T-FERAM application. In 2017 IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2017, pp 19.6.1-19.6.4.
[118]
Li, J.; Nagaraj, B.; Liang, H.; Cao, W.; Lee, C. H.; Ramesh, R. Ultrafast polarization switching in thin-film ferroelectrics. Appl. Phys. Lett. 2004, 84, 1174-1176.
[119]
Huang, H. Y.; Ge, C.; Zhang, Q. H.; Liu, C. X.; Du, J. Y.; Li, J. K.; Wang, C.; Gu, L.; Yang, G. Z.; Jin, K. J. Electrolyte-gated synaptic transistor with oxygen ions. Adv. Funct. Mater. 2019, 29, 1902702.
[120]
Ling, H. F.; Koutsouras, D. A.; Kazemzadeh, S.; van de Burgt, Y.; Yan, F.; Gkoupidenis, P. Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing. Appl. Phys. Rev. 2020, 7, 011307.
[121]
Lashkare, S.; Subramoney, S.; Ganguly, U. Nanoscale side-contact enabled three terminal Pr0.7Ca0.3MnO3 resistive random access memory for in-memory computing. IEEE Electron Device Lett. 2020, 41, 1344-1347.
[122]
Fuller, E. J.; El Gabaly, F.; Léonard, F.; Agarwal, S.; Plimpton, S. J.; Jacobs-Gedrim, R. B.; James, C. D.; Marinella, M. J.; Talin, A. A. Li-ion synaptic transistor for low power analog computing. Adv. Mater. 2017, 29, 1604310.
[123]
Nikam, R. D.; Kwak, M.; Lee, J.; Rajput, K. G.; Hwang, H. Controlled ionic tunneling in lithium nanoionic synaptic transistor through atomically thin graphene layer for neuromorphic computing. Adv. Electron. Mater. 2020, 6, 1901100.
[124]
Chen, A. Memory selector devices and crossbar array design: A modeling-based assessment. J. Comput. Electron. 2017, 16, 1186-1200.
[125]
Woo, J.; Yu, S. M. Impact of selector devices in analog RRAM-based crossbar arrays for inference and training of neuromorphic system. IEEE Trans. Very Large Scale Integrat. Syst. 2019, 27, 2205-2212.
[126]
Wang, R. P.; Yang, J. Q.; Mao, J. Y.; Wang, Z. P.; Wu, S.; Zhou, M. J.; Chen, T. Y.; Han, S. T. Recent advances of volatile memristors: Devices, mechanisms, and applications. Adv. Intell. Syst. 2020, 2, 2000055.
[127]
Imada, M.; Fujimori, A.; Tokura, Y. Metal-insulator transitions. Rev. Mod. Phys. 1998, 70, 1039-1263.
[128]
Eyert, V. The metal-insulator transitions of VO2: A band theoretical approach. Ann. Phys. 2002, 11, 650-704.
[129]
Stefanovich, G.; Pergament, A.; Stefanovich, D. Electrical switching and Mott transition in VO2. J. Phys. Condens. Matter 2000, 12, 8837-8845.
[130]
Cha, E.; Park, J.; Woo, J.; Lee, D.; Prakash, A.; Hwang, H. Comprehensive scaling study of NbO2 insulator-metal-transition selector for cross point array application. Appl. Phys. Lett. 2016, 108, 153502.
[131]
Wegkamp, D.; Stähler, J. Ultrafast dynamics during the photoinduced phase transition in VO2. Prog. Surf. Sci. 2015, 90, 464-502.
[132]
Goodenough, J. B. The two components of the crystallographic transition in Vo2. J. Solid State Chem. 1971, 3, 490-500.
[133]
Manca, N.; Kanki, T.; Tanaka, H.; Marré, D.; Pellegrino, L. Influence of thermal boundary conditions on the current-driven resistive transition in VO2 microbridges. Appl. Phys. Lett. 2015, 107, 143509.
[134]
Quackenbush, N. F.; Tashman, J. W.; Mundy, J. A.; Sallis, S.; Paik, H.; Misra, R.; Moyer, J. A.; Guo, J. H.; Fischer, D. A.; Woicik, J. C. et al. Nature of the metal insulator transition in ultrathin epitaxial vanadium dioxide. Nano Lett. 2013, 13, 4857-4861.
[135]
Lee, S.; Ivanov, I. N.; Keum, J. K.; Lee, H. N. Epitaxial stabilization and phase instability of VO2 polymorphs. Sci. Rep. 2016, 6, 19621.
[136]
Xue, W. H.; Liu, G.; Zhong, Z. C.; Dai, Y. H.; Shang, J.; Liu, Y. W.; Yang, H. L.; Yi, X. H.; Tan, H. W.; Pan, L. et al. A 1D vanadium dioxide nanochannel constructed via electric-field-induced ion transport and its superior metal-insulator transition. Adv. Mater. 2017, 29, 1702162.
[137]
Wang, Z. R.; Rao, M. Y.; Midya, R.; Joshi, S.; Jiang, H.; Lin, P.; Song, W. H.; Asapu, S.; Zhuo, Y.; Li, C. et al. Threshold switching: Threshold switching of Ag or Cu in dielectrics: Materials, mechanism, and applications (Adv. Funct. Mater. 6/2018). Adv. Funct. Mater. 2018, 28, 1870036.
[138]
Midya, R.; Wang, Z. R.; Zhang, J. M.; Savel'ev, S. E.; Li, C.; Rao, M. Y.; Jang, M. H.; Joshi, S.; Jiang, H.; Lin, P. et al. Anatomy of Ag/hafnia-based selectors with 1010 nonlinearity. Adv. Mater. 2017, 29, 1604457.
[139]
Valov, I.; Linn, E.; Tappertzhofen, S.; Schmelzer, S.; van den Hurk, J.; Lentz, F.; Waser, R. Nanobatteries in redox-based resistive switches require extension of memristor theory. Nat. Commun. 2013, 4, 1771.
[140]
van den Hurk, J.; Linn, E.; Zhang, H. H.; Waser, R.; Valov, I. Volatile resistance states in electrochemical metallization cells enabling non-destructive readout of complementary resistive switches. Nanotechnology 2014, 25, 425202.
[141]
Wang, Z. R.; Joshi, S.; Savel’ev, S. E.; Jiang, H.; Midya, R.; Lin, P.; Hu, M.; Ge, N.; Strachan, J. P.; Li, Z. Y. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 2017, 16, 101-108.
[142]
Sun, H. T.; Liu, Q.; Li, C. F.; Long, S. B.; Lv, H. B.; Bi, C.; Huo, Z. L.; Li, L.; Liu, M. Direct observation of conversion between threshold switching and memory switching induced by conductive filament morphology. Adv. Funct. Mater. 2014, 24, 5679-5686.
[143]
Wang, W.; Wang, M.; Ambrosi, E.; Bricalli, A.; Laudato, M.; Sun, Z.; Chen, X. D.; Ielmini, D. Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices. Nat. Commun. 2019, 10, 81.
[144]
Herring, C. Effect of change of scale on sintering phenomena. J. Appl. Phys. 1950, 21, 301-303.
[145]
Li, W. X.; Wang, F.; Zhang, J. W.; Li, C.; Wei, J. Q.; Shen, J. Q.; Shan, X.; Ren, T. L.; Zhao, J. S.; Song, Z. T. et al. Dual-functional nonvolatile and volatile memory in resistively switching indium tin oxide/HfOx devices. Phys. Status Solidi A 2019, 216, 1900555.
[146]
Wu, C. X.; Kim, T. W.; Choi, H. Y.; Strukov, D. B.; Yang, J. J. Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability. Nat. Commun. 2017, 8, 752.
[147]
van de Burgt, Y.; Lubberman, E.; Fuller, E. J.; Keene, S. T.; Faria, G. C.; Agarwal, S.; Marinella, M. J.; Talin, A. A.; Salleo, A. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 2017, 16, 414-418.
[148]
Lv, Z. Y.; Zhou, Y.; Han, S. T.; Roy, V. A. L. From biomaterial-based data storage to bio-inspired artificial synapse. Mater. Today 2018, 21, 537-552.
[149]
Feldman, D. E. Spike timing-dependent plasticity. In Neural Circuit and Cognitive Development: Comprehensive Developmental Neuroscience; Rubenstein, J.; Rakic, P.; Chen, B.; Kwan, K. Y., Eds.; Academic Press: New York, 2020; pp 127-141.
[150]
Prezioso, M.; Mahmoodi, M. R.; Bayat, F. M.; Nili, H.; Kim, H.; Vincent, A.; Strukov, D. B. Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits. Nat. Commun. 2018, 9, 5311.
[151]
Roberts, P. D.; Bell, C. C. Spike timing dependent synaptic plasticity in biological systems. Biol. Cybern. 2002, 87, 392-403.
[152]
Caporale, N.; Dan, Y. Spike timing-dependent plasticity: A Hebbian learning rule. Annu. Rev. Neurosci. 2008, 31, 25-46.
[153]
Jo, S. H.; Chang, T.; Ebong, I.; Bhadviya, B. B.; Mazumder, P.; Lu, W. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 2010, 10, 1297-1301.
[154]
Li, Y.; Zhong, Y. P.; Zhang, J. J.; Xu, L.; Wang, Q.; Sun, H. J.; Tong, H.; Cheng, X. M.; Miao, X. S. Activity-dependent synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems. Sci. Rep. 2014, 4, 4906.
[155]
Abbott, L. F.; Nelson, S. B. Synaptic plasticity: Taming the beast. Nat. Neurosci. 2000, 3, 1178-1183.
[156]
Foncelle, A.; Mendes, A.; Jedrzejewska-Szmek, J.; Valtcheva, S.; Berry, H.; Blackwell, K. T.; Venance, L. Modulation of spike-timing dependent plasticity: Towards the inclusion of a third factor in computational models. Front. Comput. Neurosci. 2018, 12, 49.
[157]
Chung, S.; Li, X. R.; Nelson, S. B. Short-term depression at thalamocortical synapses contributes to rapid adaptation of cortical sensory responses in vivo. Neuron 2002, 34, 437-446.
[158]
Anwar, H.; Li, X. P.; Bucher, D.; Nadim, F. Functional roles of short-term synaptic plasticity with an emphasis on inhibition. Curr. Opin. Neurobiol. 2017, 43, 71-78.
[159]
Zhang, X. L.; Guariglia, S. R.; McGlothan, J. L.; Stansfield, K. H.; Stanton, P. K.; Guilarte, T. R. Presynaptic mechanisms of lead neurotoxicity: Effects on vesicular release, vesicle clustering and mitochondria number. PLoS One 2015, 10, e0127461.
[160]
Purves, D.; Augustine, G. J.; Fitzpatrick, D.; Hall, W. C.; LaMantia, A. S.; Mooney, R. D.; Platt, M. L.; White, L. E. Neuroscience, 6th ed.; Oxford University Press: New York, 2018.
[161]
Sassone, J.; Serratto, G.; Valtorta, F.; Silani, V.; Passafaro, M.; Ciammola, A. The synaptic function of parkin. Brain 2017, 140, 2265-2272.
[162]
Abraham, W. C. Metaplasticity: Tuning synapses and networks for plasticity. Nat. Rev. Neurosci. 2008, 9, 387-399.
[163]
Crestani, A. P.; Krueger, J. N.; Barragan, E. V.; Nakazawa, Y.; Nemes, S. E.; Quillfeldt, J. A.; Gray, J. A.; Wiltgen, B. J. Metaplasticity contributes to memory formation in the hippocampus. Neuropsychopharmacology 2019, 44, 408-414.
[164]
Abraham, W. C.; Bear, M. F. Metaplasticity: The plasticity of synaptic plasticity. Trends Neurosci. 1996, 19, 126-130.
[165]
McHail, D. G.; Dumas, T. C. Multiple forms of metaplasticity at a single hippocampal synapse during late postnatal development. Dev. Cogn. Neurosci. 2015, 12, 145-154.
[166]
Camuñas-Mesa, L. A.; Linares-Barranco, B.; Serrano-Gotarredona, T. Neuromorphic spiking neural networks and their memristor-CMOS hardware implementations. Materials 2019, 12, 2745.
[167]
Huang, H. M.; Yang, R.; Tan, Z. H.; He, H. K.; Zhou, W.; Xiong, J.; Guo, X. Quasi-Hodgkin-Huxley neurons with leaky integrate-and-fire functions physically realized with memristive devices. Adv. Mater. 2019, 31, 1803849.
[168]
Barnett, M. W.; Larkman, P. M. The action potential. Pract. Neurol. 2007, 7, 192-197.
[169]
Fuortes, M. G.; Mantegazzini, F. Interpretation of the repetitive firing of nerve cells. J. Gen. Physiol. 1962, 45, 1163-1179.
[170]
Prezioso, M.; Bayat, F. M.; Hoskins, B.; Likharev, K.; Strukov, D. Self-adaptive spike-time-dependent plasticity of metal-oxide memristors. Sci. Rep. 2016, 6, 21331.
[171]
Yang, R.; Huang, H. M.; Hong, Q. H.; Yin, X. B.; Tan, Z. H.; Shi, T.; Zhou, Y. X.; Miao, X. S.; Wang, X. P.; Mi, S. B. et al. Synaptic suppression triplet-STDP learning rule realized in second-order memristors. Adv. Funct. Mater. 2018, 28, 1704455.
[172]
Zhang, X. M.; Liu, S.; Zhao, X. L.; Wu, F. C.; Wu, Q. T.; Wang, W.; Cao, R. R.; Fang, Y. L.; Lv, H. B.; Long, S. B. et al. Emulating short-term and long-term plasticity of bio-synapse based on Cu/a-Si/Pt memristor. IEEE Electron Device Lett. 2017, 38, 1208-1211.
[173]
Cai, W. R.; Ellinger, F.; Tetzlaff, R. Neuronal synapse as a memristor: Modeling pair- and triplet-based STDP rule. IEEE Trans. Biomed. Circuits Syst. 2015, 9, 87-95.
[174]
Wu, Q. T.; Wang, H.; Luo, Q.; Banerjee, W.; Cao, J. C.; Zhang, X. M.; Wu, F. C.; Liu, Q.; Li, L.; Liu, M. Full imitation of synaptic metaplasticity based on memristor devices. Nanoscale 2018, 10, 5875-5881.
[175]
Azghadi, M. R.; Linares-Barranco, B.; Abbott, D.; Leong, P. H. W. A hybrid CMOS-memristor neuromorphic synapse. IEEE Trans. Biomed. Circuits Syst. 2017, 11, 434-445.
[176]
La Barbera, S.; Vincent, A. F.; Vuillaume, D.; Querlioz, D.; Alibart, F. Interplay of multiple synaptic plasticity features in filamentary memristive devices for neuromorphic computing. Sci. Rep. 2016, 6, 39216.
[177]
Zarudnyi, K.; Mehonic, A.; Montesi, L.; Buckwell, M.; Hudziak, S.; Kenyon, A. J. Spike-timing dependent plasticity in unipolar silicon oxide RRAM devices. Front. Neurosci. 2018, 12, 57.
[178]
Ambrogio, S.; Ciocchini, N.; Laudato, M.; Milo, V.; Pirovano, A.; Fantini, P.; Ielmini, D. Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM) synapses. Front. Neurosci. 2016, 10, 56.
[179]
Guo, R.; Zhou, Y. X.; Wu, L. J.; Wang, Z. R.; Lim, Z. S.; Yan, X. B.; Lin, W. N.; Wang, H.; Yoong, H. Y.; Chen, S. H. et al. Control of synaptic plasticity learning of ferroelectric tunnel memristor by nanoscale interface engineering. ACS Appl. Mater. Interfaces 2018, 10, 12862-12869.
[180]
Li, Y.; Zhong, Y. P.; Xu, L.; Zhang, J. J.; Xu, X. H.; Sun, H. J.; Miao, X. S. Ultrafast synaptic events in a chalcogenide memristor. Sci. Rep. 2013, 3, 1619.
[181]
La Barbera, S.; Vuillaume, D.; Alibart, F. Filamentary switching: Synaptic plasticity through device volatility. ACS Nano 2015, 9, 941-949.
[182]
Zhang, Y. S.; Zhong, S.; Song, L.; Ji, X. L.; Zhao, R. Emulating dynamic synaptic plasticity over broad timescales with memristive device. Appl. Phys. Lett. 2018, 113, 203102.
[183]
Bennett, C. H.; La Barbera, S.; Vincent, A. F.; Klein, J. O.; Alibart, F.; Querlioz, D. Exploiting the short-term to long-term plasticity transition in memristive nanodevice learning architectures. In IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 2016, pp 947-954.
[184]
Kim, M.-K.; Lee, J.-S. Short-term plasticity and long-term potentiation in artificial biosynapses with diffusive dynamics. ACS Nano 2018, 12, 1680-1687.
[185]
Atkinson, R. C.; Shiffrin, R. M. Human memory: A proposed system and its control processes. Psychol. Learn. Motiv. 1968, 2, 89-195.
[186]
Moon, K.; Kwak, M.; Park, J.; Lee, D.; Hwang, H. Improved conductance linearity and conductance ratio of 1T2R synapse device for neuromorphic systems. IEEE Electron Device Lett. 2017, 38, 1023-1026.
[187]
Chandrasekaran, S.; Simanjuntak, F. M.; Saminathan, R.; Panda, D.; Tseng, T. Y. Improving linearity by introducing Al in HfO2 as a memristor synapse device. Nanotechnology 2019, 30, 445205.
[188]
Han, H.; Yu, H. Y.; Wei, H. H.; Gong, J. D.; Xu, W. T. Recent progress in three-terminal artificial synapses: From device to system. Small 2019, 15, 1900695.
[189]
Wan, Q. Z.; Sharbati, M. T.; Erickson, J. R.; Du, Y. H.; Xiong, F. Emerging artificial synaptic devices for neuromorphic computing. Adv. Mater. Technol. 2019, 4, 1900037.
[190]
Nikam, R. D.; Kwak, M.; Lee, J.; Rajput, K. G.; Banerjee, W.; Hwang, H. Near ideal synaptic functionalities in Li ion synaptic transistor using Li3POxSex electrolyte with high ionic conductivity. Sci. Rep. 2019, 9, 18883.
[191]
Zhu, J. D.; Yang, Y. C.; Jia, R. D.; Liang, Z. X.; Zhu, W.; Rehman, Z. U.; Bao, L.; Zhang, X. X.; Cai, Y. M.; Song, L. et al. Ion gated synaptic transistors based on 2D van der Waals crystals with tunable diffusive dynamics. Adv. Mater. 2018, 30, 1800195.
[192]
Zhu, X. J.; Du, C.; Jeong, Y.; Lu, W. D. Emulation of synaptic metaplasticity in memristors. Nanoscale 2017, 9, 45-51.
[193]
Yang, Y. C.; Yin, M. H.; Yu, Z. Z.; Wang, Z. W.; Zhang, T.; Cai, Y. M.; Lu, W. D.; Huang, R. Multifunctional nanoionic devices enabling simultaneous heterosynaptic plasticity and efficient in-memory boolean logic. Adv. Electron. Mater. 2017, 3, 1700032.
[194]
Chistiakova, M.; Bannon, N. M.; Bazhenov, M.; Volgushev, M. Heterosynaptic plasticity: Multiple mechanisms and multiple roles. Neuroscientist 2014, 20, 483-498.
[195]
Bailey, C. H.; Giustetto, M.; Huang, Y. Y.; Hawkins, R. D.; Kandel, E. R. Is heterosynaptic modulation essential for stabilizing hebbian plasiticity and memory? Nat. Rev. Neurosci. 2000, 1, 11-20.
[196]
Keene, S. T.; Lubrano, C.; Kazemzadeh, S.; Melianas, A.; Tuchman, Y.; Polino, G.; Scognamiglio, P.; Cinà, L.; Salleo, A.; van de Burgt, Y. et al. A biohybrid synapse with neurotransmitter-mediated plasticity. Nat. Mater. 2020, 19, 969-973.
[197]
Stoliar, P.; Tranchant, J.; Corraze, B.; Janod, E.; Besland, M. P.; Tesler, F.; Rozenberg, M.; Cario, L. A leaky-integrate-and-fire neuron analog realized with a Mott insulator. Adv. Funct. Mater. 2017, 27, 1604740.
[198]
Zhang, Y. S.; He, W.; Wu, Y. J.; Huang, K. J.; Shen, Y. S.; Su, J. S.; Wang, Y. Y.; Zhang, Z. Y.; Ji, X. L.; Li, G. Q. et al. Highly compact artificial memristive neuron with low energy consumption. Small 2018, 14, 1802188.
[199]
Hu, X. Y.; Liu, C. X. Dynamic property analysis and circuit implementation of simplified memristive Hodgkin-Huxley neuron model. Nonlinear Dyn. 2019, 97, 1721-1733.
[200]
Yi, W.; Tsang, K. K.; Lam, S. K.; Bai, X. W.; Crowell, J. A.; Flores, E. A. Biological plausibility and stochasticity in scalable VO2 active memristor neurons. Nat. Commun. 2018, 9, 4661.
[201]
Mehonic, A.; Kenyon, A. J. Emulating the electrical activity of the neuron using a silicon oxide RRAM cell. Front. Neurosci. 2016, 10, 57.
[202]
Li, C.; Belkin, D.; Li, Y. N.; Yan, P.; Hu, M.; Ge, N.; Jiang, H.; Montgomery, E.; Lin, P.; Wang, Z. R. et al. Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nat. Commun. 2018, 9, 2385.
[203]
Abadi, M.; Barham, P.; Chen, J. M.; Chen, Z. F.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M. et al. TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, Savannah, GA, USA, 2016, pp 265-283.
[204]
Duan, Q. X.; Jing, Z. K.; Zou, X. L.; Wang, Y. H.; Yang, K.; Zhang, T.; Wu, S.; Huang, R.; Yang, Y. C. Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks. Nat. Commun. 2020, 11, 3399.
[205]
Pantazi, A.; Woźniak, S.; Tuma, T.; Eleftheriou, E. All-memristive neuromorphic computing with level-tuned neurons. Nanotechnology 2016, 27, 355205.
[206]
Liu, L.; Xiong, W.; Liu, Y. X.; Chen, K. G.; Xu, Z.; Zhou, Y.; Han, J.; Ye, C.; Chen, X.; Song, Z. T. et al. Designing high-performance storage in HfO2/BiFeO3 memristor for artificial synapse applications. Adv. Electron. Mater. 2020, 6, 1901012.
[207]
Lin, C. Y.; Chen, J.; Chen, P. H.; Chang, T. C.; Wu, Y. T.; Eshraghian, J. K.; Moon, J.; Yoo, S.; Wang, Y. H.; Chen, W. C. et al. Adaptive synaptic memory via lithium ion modulation in RRAM devices. Small 2020, 16, 2003964.
[208]
Wang, W. J.; Song, W. D.; Liu, J. C.; Zhuo, V. Y. Q.; Lee, H. K.; Wang, I. T.; Li, M. H.; Chen, Z. X.; Chui, K. J.; Zhu, Y. Endurance and variability control for analog switching in dual oxide layer RRAM devices. In 2020 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA), Singapore, 2020, pp 1-4.
[209]
Abbas, H.; Abbas, Y.; Hassan, G.; Sokolov, A. S.; Jeon, Y. R.; Ku, B.; Kang, C. J.; Choi, C. The coexistence of threshold and memory switching characteristics of ALD HfO2 memristor synaptic arrays for energy-efficient neuromorphic computing. Nanoscale 2020, 12, 14120-14134.
[210]
Lashkare, S.; Chouhan, S.; Chavan, T.; Bhat, A.; Kumbhare, P.; Ganguly, U. PCMO RRAM for integrate-and-fire neuron in spiking neural networks. IEEE Electron Device Lett. 2018, 39, 484-487.
[211]
Tuma, T.; Pantazi, A.; Le Gallo, M.; Sebastian, A.; Eleftheriou, E. Stochastic phase-change neurons. Nat. Nanotechnol. 2016, 11, 693-699.
[212]
Dutta, S.; Kumar, V.; Shukla, A.; Mohapatra, N. R.; Ganguly, U. Leaky integrate and fire neuron by charge-discharge dynamics in floating-body MOSFET. Sci. Rep. 2017, 7, 8257.
Publication history
Copyright
Acknowledgements
Publication history
Received: 31 December 2020
Revised: 28 February 2021
Accepted: 12 March 2021
Published:
13 April 2021
Issue date: September 2021
Copyright
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
Acknowledgements
This work was supported by Samsung Electronics Co., Ltd (No. IO201214-08153-01). This work was supported by Convergent Technology R&D Program for Human Augmentation through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (No. NRF-2020M3C1B8081519). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIP) (No. NRF-2020M3F3A2A02082445).
FEEDBACK 
TOP