Journal Home > Volume 15 , Issue 9
Nano Research 2022, 15(9): 8410-8418 https://doi.org/10.1007/s12274-022-4416-1
Research Article | Issue | Published: 19 May 2022

Memristive structure of Nb/HfOx/Pd with controllable switching mechanisms to perform featured actions in neuromorphic networks

Show Author's Information Junwei Yu1,  Fei Zeng1,2( ),  Qin Wan1,  Yiming Sun1,  Leilei Qiao1,  Tongjin Chen1,  Huaqiang Wu2,3,  Zhen Zhao4,  Jiangli Cao4,  Feng Pan1
Key Laboratory of Advanced Materials (MOE), School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
Center for Brain Inspired Computing Research (CBICR), Tsinghua University, Beijing 100084, China
Microelectronics Institute, Tsinghua University, Beijing 100084, China
School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
Keywords:
synaptic plasticity, oxygen vacancy, phase transition, memristive system, action potential
Topics:
Electron devices
Cite this article:
Yu J, Zeng F, Wan Q, et al. Memristive structure of Nb/HfOx/Pd with controllable switching mechanisms to perform featured actions in neuromorphic networks. Nano Research, 2022, 15(9): 8410-8418. https://doi.org/10.1007/s12274-022-4416-1
Download citation
EndNote(RIS)
BibTeX

601

Views

44

Downloads

Citations

3

Crossref

2

WoS

2

Scopus

0

CSCD

Abstract Full text Electronic supplementary material About this article

All memristor neuromorphic networks have great potential and advantage in both technology and computational protocols for artificial intelligence. It is crucial to find suitable elementary units for both performing featured neuromorphic functions and fabrication in large scale. Here a simple memristive structure, Nb/HfOx/Pd, is proposed for this goal. Its two resistive switching mechanisms, Mott transition of NbO2 and oxygen vacancy (Vo) migration, can be controlled by modulating external bias directions. Negative bias activates reversible phase transition and restrains Vo filament formation to allow the memristor to mimic the firing action potential. Positive bias activates Vo filament formation and restrains the other to allow the memristor to mimic synaptic plasticity and learning protocols. The system can respond adaptively to naturally generated action potentials and modified synaptic signals from the same memristive structure. In addition, some special features related to signal encoding and recognition are discovered when the system is settled according to chaos circuit theory. Our study provides a novel approach for designing elementary units for neuromorphic computations.


menu
Abstract
Full text
Outline
Electronic supplementary material
About this article

Memristive structure of Nb/HfOx/Pd with controllable switching mechanisms to perform featured actions in neuromorphic networks

Show Author's information Junwei Yu1, Fei Zeng1,2( ), Qin Wan1, Yiming Sun1, Leilei Qiao1, Tongjin Chen1, Huaqiang Wu2,3, Zhen Zhao4, Jiangli Cao4, Feng Pan1
Key Laboratory of Advanced Materials (MOE), School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
Center for Brain Inspired Computing Research (CBICR), Tsinghua University, Beijing 100084, China
Microelectronics Institute, Tsinghua University, Beijing 100084, China
School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China

Abstract

All memristor neuromorphic networks have great potential and advantage in both technology and computational protocols for artificial intelligence. It is crucial to find suitable elementary units for both performing featured neuromorphic functions and fabrication in large scale. Here a simple memristive structure, Nb/HfOx/Pd, is proposed for this goal. Its two resistive switching mechanisms, Mott transition of NbO2 and oxygen vacancy (Vo) migration, can be controlled by modulating external bias directions. Negative bias activates reversible phase transition and restrains Vo filament formation to allow the memristor to mimic the firing action potential. Positive bias activates Vo filament formation and restrains the other to allow the memristor to mimic synaptic plasticity and learning protocols. The system can respond adaptively to naturally generated action potentials and modified synaptic signals from the same memristive structure. In addition, some special features related to signal encoding and recognition are discovered when the system is settled according to chaos circuit theory. Our study provides a novel approach for designing elementary units for neuromorphic computations.

Keywords: synaptic plasticity, oxygen vacancy, phase transition, memristive system, action potential

References(50)

1

Sung, S. H.; Kim, T. J.; Shin, H.; Namkung, H.; Im, T. H.; Wang, H. S.; Lee, K. J. Memory-centric neuromorphic computing for unstructured data processing. Nano Res. 2021, 14, 3126–3142.
DOI Google Scholar
2

Zhang, Z. C.; Li, Y.; Wang, J. J.; Qi, D. H.; Yao, B. W.; Yu, M. X.; Chen, X. D.; Lu, T. B. Synthesis of wafer-scale graphdiyne/graphene heterostructure for scalable neuromorphic computing and artificial visual systems. Nano Res. 2021, 14, 4591–4600.
DOI Google Scholar
3
Horowitz, M. Computing’s energy problem (and what we can do about it). In Proceedings of 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), San Francisco, CA, USA, 2014, pp 10–14.https://doi.org/10.1109/ISSCC.2014.6757323
DOI
4

Kumar, S.; Strachan, J. P.; Williams, R. S. Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing. Nature 2017, 548, 318–321.
DOI Google Scholar
5

Wang, Z. R.; Joshi, S.; Savel'ev, S.; Song, W. H.; Midya, R.; Li, Y. N.; Rao, M. Y.; Yan, P.; Asapu, S.; Zhuo, Y. et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 2018, 1, 137–145.
DOI Google Scholar
6

Wang, T. Y.; Meng, J. L.; Rao, M. Y.; He, Z. Y.; Chen, L.; Zhu, H.; Sun, Q. Q.; Ding, S. J.; Bao, W. Z.; Zhou, P. et al. Three-dimensional nanoscale flexible memristor networks with ultralow power for information transmission and processing application. Nano Lett. 2020, 20, 4111–4120.
DOI Google Scholar
7

Yu, S. M.; Gao, B.; Fang, Z.; Yu, H. Y.; Kang, J. F.; Wong, H. S. P. A low energy oxide-based electronic synaptic device for neuromorphic visual systems with tolerance to device variation. Adv. Mater. 2013, 25, 1774–1779.
DOI Google Scholar
8

Yao, P.; Wu, H. Q.; Gao, B.; Tang, J. S.; Zhang, Q. T.; Zhang, W. Q.; Yang, J. J.; Qian, H. Fully hardware-implemented memristor convolutional neural network. Nature 2020, 577, 641–646.
DOI Google Scholar
9

Kumar, S.; Williams, R. S.; Wang, Z. W. Third-order nanocircuit elements for neuromorphic engineering. Nature 2020, 585, 518–523.
DOI Google Scholar
10

Jeong, Y.; Lee, J.; Moon, J.; Shin, J. H.; Lu, W. D. K-means data clustering with memristor networks. Nano Lett. 2018, 18, 4447–4453.
DOI Google Scholar
11

Choi, S.; Shin, J. H.; Lee, J.; Sheridan, P.; Lu, W. D. Experimental demonstration of feature extraction and dimensionality reduction using memristor networks. Nano Lett. 2017, 17, 3113–3118.
DOI Google Scholar
12

Poddar, S.; Zhang, Y. T.; Gu, L. L.; Zhang, D. Q.; Zhang, Q. P.; Yan, S.; Kam, M.; Zhang, S. F.; Song, Z. T.; Hu, W. D. et al. Down-scalable and ultra-fast memristors with ultra-high density three-dimensional arrays of perovskite quantum wires. Nano Lett. 2021, 21, 5036–5044.
DOI Google Scholar
13

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.
DOI Google Scholar
14

Wang, Z. R.; Rao, M. Y.; Han, J. W.; Zhang, J. M.; Lin, P.; Li, Y. N.; Li, C.; Song, W. H.; Asapu, S.; Midya, R. et al. Capacitive neural network with neuro-transistors. Nat. Commun. 2018, 9, 3208.
DOI Google Scholar
15

Wan, Q.; Zeng, F.; Yin, J.; Sun, Y. M.; Hu, Y. D.; Liu, J. L.; Wang, Y. C.; Li, G. Q.; Guo, D.; Pan, F. Phase-change nanoclusters embedded in a memristor for simulating synaptic learning. Nanoscale 2019, 11, 5684–5692.
DOI Google Scholar
16

Wan, Q.; Zeng, F.; Sun, Y. M.; Chen, T. J.; Yu, J. W.; Wu, H. Q.; Zhao, Z.; Cao, J. L.; Pan, F. Memristive behaviors dominated by reversible nucleation dynamics of phase-change nanoclusters. Small 2022, 18, 2105070.
DOI Google Scholar
17

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.
DOI Google Scholar
18

Gibson, G. A.; Musunuru, S.; Zhang, J. M.; Vandenberghe, K.; Lee, J.; Hsieh, C. C.; Jackson, W.; Jeon, Y.; Henze, D.; Li, Z. Y. et al. An accurate locally active memristor model for S-type negative differential resistance in NbOx. Appl. Phys. Lett. 2016, 108, 023505.
DOI Google Scholar
19

O'Hara, A.; Demkov, A. A. Nature of the metal-insulator transition in NbO2. Phys. Rev. B 2015, 91, 094305.
DOI Google Scholar
20

Fajardo, G. J. P.; Howard, S. A.; Evlyukhin, E.; Wahila, M. J.; Mondal, W. R.; Zuba, M.; Boschker, J. E.; Paik, H.; Schlom, D. G.; Sadowski, J. T. et al. Structural phase transitions of NbO2: Bulk versus surface. Chem. Mater. 2021, 33, 1416–1425.
DOI Google Scholar
21

Li, S.; Liu, X. J.; Nandi, S. K.; Nath, S. K.; Elliman, R. G. Origin of current-controlled negative differential resistance modes and the emergence of composite characteristics with high complexity. Adv. Funct. Mater. 2019, 29, 1905060.
DOI Google Scholar
22

Nandi, S. K.; Nath, S. K.; El-Helou, A. E.; Li, S.; Liu, X. J.; Raad, P. E.; Elliman, R. G. Current localization and redistribution as the basis of discontinuous current controlled negative differential resistance in NbOx. Adv. Funct. Mater. 2019, 29, 1906731.
DOI Google Scholar
23

Nandi, S. K.; Nath, S. K.; El-Helou, A. E.; Li, S.; Ratcliff, T.; Uenuma, M.; Raad, P. E.; Elliman, R. G. Electric field- and current-induced electroforming modes in NbOx. ACS Appl. Mater. Interfaces 2020, 12, 8422–8428.
DOI Google Scholar
24

Liu, K.; Lee, S.; Yang, S.; Delaire, O.; Wu, J. Q. Recent progresses on physics and applications of vanadium dioxide. Mater. Today 2018, 21, 875–896.
DOI Google Scholar
25

Sohn, J. I.; Joo, H. J.; Ahn, D.; Lee, H. H.; Porter, A. E.; Kim, K.; Kang, D. J.; Welland, M. E. Surface-stress-induced Mott transition and nature of associated spatial phase transition in single crystalline VO2 nanowires. Nano Lett. 2009, 9, 3392–3397.
DOI Google Scholar
26

Pan, F.; Gao, S.; Chen, C.; Song, C.; Zeng, F. Recent progress in resistive random access memories: Materials, switching mechanisms, and performance. Mater. Sci. Eng. R Rep. 2014, 83, 1–59.
DOI Google Scholar
27

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.
DOI Google Scholar
28

Shen, Z. J.; Zhao, C.; Zhao, T. S.; Xu, W. Y.; Liu, Y. N.; Qi, Y. F.; Mitrovic, I. Z.; Yang, L.; Zhao, C. Z. Artificial synaptic performance with learning behavior for memristor fabricated with stacked solution-processed switching layers. ACS Appl. Electron. Mater. 2021, 3, 1288–1300.
DOI Google Scholar
29

Lee, M. J.; Lee, C. B.; Lee, D.; Lee, S. R.; Chang, M.; Hur, J. H.; Kim, Y. B.; Kim, C. J.; Seo, D. H.; Seo, S. et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5−x/TaO2−x bilayer structures. Nat. Mater. 2011, 10, 625–630.
DOI Google Scholar
30
Gao, B. ; Wu, H. Q. ; Wu, W. ; Wang, X. H. ; Yao, P. ; Xi, Y. ; Zhang, W. Q. ; Deng, N. ; Huang, P. ; Liu, X. Y. et al. Modeling disorder effect of the oxygen vacancy distribution in filamentary analog RRAM for neuromorphic computing. In Proceedings of 2017 IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2017, pp 4.4.1–4.4.4.https://doi.org/10.1109/IEDM.2017.8268326
DOI
31

Yin, J.; Zeng, F.; Wan, Q.; Li, F.; Sun, Y. M.; Hu, Y. D.; Liu, J. L.; Li, G. Q.; Pan, F. Adaptive crystallite kinetics in homogenous bilayer oxide memristor for emulating diverse synaptic plasticity. Adv. Funct. Mater. 2018, 28, 1706927.
DOI Google Scholar
32
Fu, Y. Y. ; Zhou, Y. ; Huang, X. D. ; Dong, B. Y. ; Zhuge, F. W. ; Li, Y. ; He, Y. H. ; Chai, Y. ; Miao, X. S. Reconfigurable synaptic and neuronal functions in a V/VOx/HfWOx/Pt memristor for nonpolar spiking convolutional neural network. Adv. Funct. Mater., in press, https://doi.org/10.1002/adfm.202111996.
DOI
33

Nandi, S. K.; Liu, X. J.; Venkatachalam, D. K.; Elliman, R. G. Self-assembly of an NbO2 interlayer and configurable resistive switching in Pt/Nb/HfO2/Pt structures. Appl. Phys. Lett. 2015, 107, 132901.
DOI Google Scholar
34

Lin, C. Y.; Chen, P. H.; Chang, T. C.; Chang, K. C.; Zhang, S. D.; Tsai, T. M.; Pan, C. H.; Chen, M. C.; Su, Y. T.; Tseng, Y. T. et al. Attaining resistive switching characteristics and selector properties by varying forming polarities in a single HfO2-based RRAM device with a vanadium electrode. Nanoscale 2017, 9, 8586–8590.
DOI Google Scholar
35

Kim, S.; Choi, S.; Lu, W. Comprehensive physical model of dynamic resistive switching in an oxide memristor. ACS Nano 2014, 8, 2369–2376.
DOI Google Scholar
36

Pickett, M. D.; Medeiros-Ribeiro, G.; Williams, R. S. A scalable neuristor built with Mott memristors. Nat. Mater. 2013, 12, 114–117.
DOI Google Scholar
37

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.
DOI Google Scholar
38

Bi, G. Q.; Poo, M. M. Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 1998, 18, 10464–10472.
DOI Google Scholar
39

Zucker, R. S.; Regehr, W. G. Short-term synaptic plasticity. Annu. Rev. Physiol. 2002, 64, 355–405.
DOI Google Scholar
40

Chua, L. O. Local activity is the origin of complexity. Int. J. Bifurcation Chaos 2005, 15, 3435–3456.
DOI Google Scholar
41

Jin, P. P.; Wang, G. Y.; Liang, Y.; Iu, H. H. C.; Chua, L. O. Neuromorphic dynamics of Chua corsage memristor. IEEE Trans. Circuits Syst. I Regul. Pap. 2021, 68, 4419–4432.
DOI Google Scholar
42

Kumar, S.; Wang, Z. W.; Davila, N.; Kumari, N.; Norris, K. J.; Huang, X. P.; Strachan, J. P.; Vine, D.; Kilcoyne, A. L. D.; Nishi, Y. et al. Physical origins of current and temperature controlled negative differential resistances in NbO2. Nat. Commun. 2017, 8, 658.
DOI Google Scholar
43

Murphy, B. K.; Miller, K. D. Multiplicative gain changes are induced by excitation or inhibition alone. J. Neurosci. 2003, 23, 10040–10051.
DOI Google Scholar
44

Chance, F. S.; Abbott, L. F.; Reyes, A. D. Gain modulation from background synaptic input. Neuron 2002, 35, 773–782.
DOI Google Scholar
45

Shouval, H. Z.; Bear, M. F.; Cooper, L. N. A unified model of NMDA receptor-dependent bidirectional synaptic plasticity. Proc. Natl. Acad. Sci. USA. 2002, 99, 10831–10836.
DOI Google Scholar
46

Yang, S. N.; Tang, Y. G.; Zucker, R. S. Selective induction of LTP and LTD by postsynaptic [Ca2+]i elevation. J. Neurophysiol. 1999, 81, 781–787.
DOI Google Scholar
47

Dudek, S. M.; Bear, M. F. Homosynaptic long-term depression in area CA1 of hippocampus and effects of N-methyl-D-aspartate receptor blockade. Proc. Natl. Acad. Sci. USA 1992, 89, 4363–4367.
DOI Google Scholar
48

Leon, J. J. D.; Norris, K. J.; Yang, J. J.; Sevic, J. F.; Kobayashi, N. P. A niobium oxide-tantalum oxide selector-memristor self-aligned nanostack. Appl. Phys. Lett. 2017, 110, 103102.
DOI Google Scholar
49
Kandel, E. R. ; Schwartz, J. H. ; Jessell, T. M. Principles of Neural Science; 4th ed. McGraw-Hill: New York, 2000.
50
Izhikevich, E. M. Dynamical Systems in Neuroscience; The MIT Press: Cambridge, 2007.https://doi.org/10.7551/mitpress/2526.001.0001
DOI
File
12274_2022_4416_MOESM1_ESM.pdf (1 MB)
Publication history
Copyright
Acknowledgements

Publication history

Received: 12 March 2022
Revised: 07 April 2022
Accepted: 09 April 2022
Published: 19 May 2022
Issue date: September 2022

Copyright

© Tsinghua University Press 2022

Acknowledgements

Acknowledgements

This study was supported by the National Natural Science Foundation of China (No. 51972192). Furthermore, we also thank the National Center of Electron Microscopy in Beijing, Tsinghua University, for assistance with microstructural analysis.

Return