Memristors with analogue switching and high on/off ratios using a van der Waals metallic cathode | Nature Electronics

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Neuromorphic computing based on memristors could help meet the growing demand for data-intensive computing applications such as artificial intelligence. Analogue memristors with multiple conductance states are of particular use in high-efficiency neuromorphic computing, but their weight mapping capabilities are typically limited by small on/off ratios. Here we show that memristors with analogue resistive switching and large on/off ratios can be created using two-dimensional van der Waals metallic materials (graphene or platinum ditelluride) as the cathodes. The memristors use silver as the top anode and indium phosphorus sulfide as the switching medium. Previous approaches have focused on modulating ion motion using changes to the resistive switching layer or anode, which can lower the on/off ratios. In contrast, our approach relies on the van der Waals cathode, which allows silver ion intercalation/de-intercalation, creating a high diffusion barrier to modulate ion motion. The strategy can achieve analogue resistive switching with an on/off ratio up to 108, over 8-bit conductance states and attojoule-level power consumption. We use the analogue properties to perform the chip-level simulation of a convolutional neural network that offers high recognition accuracy.

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The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Lanza, M. et al. Memristive technologies for data storage, computation, encryption, and radio-frequency communication. Science 376, eabj9979 (2022).

Article Google Scholar

Kendall, J. D. & Kumar, S. The building blocks of a brain-inspired computer. Appl. Phys. Rev. 7, 011305 (2020).

Article Google Scholar

Rao, M. et al. Thousands of conductance levels in memristors integrated on CMOS. Nature 615, 823–829 (2023).

Article Google Scholar

Zhao, M., Gao, B., Tang, J., Qian, H. & Wu, H. Reliability of analogue resistive switching memory for neuromorphic computing. Appl. Phys. Rev. 7, 011301 (2020).

Article Google Scholar

Zhang, W. et al. Neuro-inspired computing chips. Nat. Electron. 3, 371–382 (2020).

Article Google Scholar

Tang, B. et al. Wafer-scale solution-processed 2D material analogue resistive memory array for memory-based computing. Nat. Commun. 13, 3037 (2022).

Article Google Scholar

Rana, A. M., Ismail, M., Akber, T., Nadeem, M. Y. & Kim, S. Transition from unipolar to bipolar, multilevel switching, abrupt and gradual reset phenomena in a TaN/CeO2/Ti/Pt memory devices. Mater. Res. Bull. 117, 41–47 (2019).

Article Google Scholar

Gawai, U., Kumar, D., Singh, A., Wu, C.-H. & Chang, K.-M. Oxygen vacancies controlled highly stable bilayer analogue synapse used for neuromorphic computing systems. ACS Appl. Electron. Mater. 4, 4265–4272 (2022).

Article Google Scholar

Mohanty, S. K. et al. Uniform resistive switching and highly stable synaptic characteristics of HfOx sandwiched TaOx-based memristor for neuromorphic system. Ceram. Int. 49, 16909–16917 (2023).

Article Google Scholar

Lu, X. F. et al. Exploring low power and ultrafast memristor on p-type van der Waals SnS. Nano Lett. 21, 8800–8807 (2021).

Article Google Scholar

Yang, Y. et al. Electrochemical dynamics of nanoscale metallic inclusions in dielectrics. Nat. Commun. 5, 4232 (2014).

Article Google Scholar

Wu, F. C. et al. Interface engineering via MoS2 insertion layer for improving resistive switching of conductive-bridging random access memory. Adv. Electron. Mater. 5, 1800747 (2019).

Article Google Scholar

Ismail, M., Abbas, H., Choi, C. & Kim, S. Controllable analogue resistive switching and synaptic characteristics in ZrO2/ZTO bilayer memristive device for neuromorphic systems. Appl. Surf. Sci. 529, 147107 (2020).

Article Google Scholar

Li, S. et al. Wafer-scale 2D hafnium diselenide based memristor crossbar array for energy-efficient neural network hardware. Adv. Mater. 34, 2103376 (2021).

Article Google Scholar

Li, Y. et al. In-memory computing using memristor arrays with ultrathin 2D PdSeOx/PdSe2 heterostructure. Adv. Mater. 34, 2201488 (2022).

Article Google Scholar

Xu, J. et al. Tunable digital-to-analogue switching in Nb2O5-based resistance switching devices by oxygen vacancy engineering. Appl. Surf. Sci. 579, 152114 (2022).

Article Google Scholar

Saleem, A. et al. Transformation of digital to analogue switching in TaOx-based memristor device for neuromorphic applications. Appl. Phys. Lett. 118, 112103 (2021).

Article Google Scholar

Hu, L. X. et al. Ultrasensitive memristive synapses based on lightly oxidized sulfide films. Adv. Mater. 29, 1606927 (2017).

Article Google Scholar

Belmonte, A. et al. Voltage-controlled reverse filament growth boosts resistive switching memory. Nano Res. 11, 4017–4025 (2018).

Article Google Scholar

Jeon, H. et al. Resistive switching behaviors of Cu/TaOx/TiN device with combined oxygen vacancy/copper conductive filaments. Curr. Appl. Phys. 15, 1005–1009 (2015).

Article Google Scholar

Chen, S. et al. Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural networks. Nat. Electron. 3, 638–645 (2020).

Article Google Scholar

Kadhim, M. S. et al. Existence of resistive switching memory and negative differential resistance state in self-colored MoS2/ZnO heterojunction devices. ACS Appl. Electron. Mater. 1, 318–324 (2019).

Article Google Scholar

Ahn, W. et al. A highly reliable molybdenum disulfide-based synaptic memristor using a copper migration-controlled structure. Small 19, 2300223 (2023).

Article Google Scholar

Xie, J., Afshari, S. & Sanchez Esqueda, I. Hexagonal boron nitride (h-BN) memristor arrays for analogue-based machine learning hardware. npj 2D Mater. Appl. 6, 50 (2022).

Article Google Scholar

Pan, C. B. et al. Coexistence of grain-boundaries-assisted bipolar and threshold resistive switching in multilayer hexagonal boron nitride. Adv. Funct. Mater. 27, 1604811 (2017).

Article Google Scholar

Yeon, H. et al. Alloying conducting channels for reliable neuromorphic computing. Nat. Nanotechnol. 15, 574–579 (2020).

Google Scholar

Weng, Z. et al. High-performance memristors based on few-layer manganese phosphorus trisulfide for neuromorphic computing. Adv. Funct. Mater. 34, 2305386 (2023).

Article Google Scholar

Weng, Z. et al. Reliable memristor crossbar array based on 2D layered nickel phosphorus trisulfide for energy-efficient neuromorphic hardware. Small 20, 2304518 (2023).

Article Google Scholar

Zhou, J. et al. Layered intercalation materials. Adv. Mater. 33, 2004557 (2021).

Article Google Scholar

Gonzalez-Rosillo, J. C. et al. Lithium-battery anode gains additional functionality for neuromorphic computing through metal–insulator phase separation. Adv. Mater. 32, 1907465 (2020).

Article Google Scholar

Zhu, X. J., Li, D., Liang, X. G. & Lu, W. D. Ionic modulation and ionic coupling effects in MoS2 devices for neuromorphic computing. Nat. Mater. 18, 141–148 (2019).

Article Google Scholar

Seo, S. et al. Artificial van der Waals hybrid synapse and its application to acoustic pattern recognition. Nat. Commun. 11, 3936 (2020).

Article Google Scholar

Xu, Y. B. et al. In situ, atomic-resolution observation of lithiation and sodiation of WS2 nanoflakes: implications for lithium-ion and sodium-ion batteries. Small 17, e2100637 (2021).

Article Google Scholar

Peng, X., Huang, S., Jiang, H., Lu, A. & Yu, S. DNN+NeuroSim V2.0: an end-to-end benchmarking framework for compute-in-memory accelerators for on-chip training. IEEE Trans. Comput.-Aided Design Integr. Circuits Syst. 40, 2306–2319 (2021).

Article Google Scholar

Peng, X. et al. DNN+NeuroSim: an end-to-end benchmarking framework for compute-in-memory accelerators with versatile device technologies. In 2019 IEEE International Electron Devices Meeting (IEDM) 32.5.1–32.5.4 (IEEE, 2019).

Burr, G. W. et al. Neuromorphic computing using non-volatile memory. Adv. Phys. X 2, 89–124 (2017).

Google Scholar

Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251–14269 (1994).

Article Google Scholar

Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953–17979 (1994).

Article Google Scholar

Hammer, B., Hansen, L. B. & Norskov, J. K. Improved adsorption energetics within density-functional theory using revised Perdew-Burke-Ernzerhof functionals. Phys. Rev. B 59, 7413–7421 (1999).

Article Google Scholar

Henkelman, G. & Jonsson, H. Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points. J. Chem. Phys. 113, 9978–9985 (2000).

Article Google Scholar

Sheppard, D., Terrell, R. & Henkelman, G. Optimization methods for finding minimum energy paths. J. Chem. Phys. 128, 134106 (2008).

Article Google Scholar

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This work is supported by the National Key R&D Program of China (no. 2018YFA0703700 (J.H.)), National Natural Science Foundation of China (nos. U23A20364 (J.H.) and 62204175 (Y.L.)), Natural Science Foundation of Jiangsu Province (no. BK20220280 (Y.L.)) and Natural Science Foundation of Hubei Province (no. 2022CFB735 (Y.L.)). We also acknowledge the Center for Electron Microscopy of Wuhan University for their substantial support.

These authors contributed equally: Yesheng Li, Yao Xiong.

Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, and School of Physical and Technology, Wuhan University, Wuhan, China

Yesheng Li, Xiaolin Zhang, Lei Yin, Yiling Yu, Hao Wang & Jun He

Suzhou Institute of Wuhan University, Suzhou, China

Yesheng Li

School of Science, Wuhan University of Technology, Wuhan, China

Yao Xiong

State Key Laboratory for Chemo/Biosensing and Chemometrics, College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha, China

Lei Liao

Wuhan Institute of Quantum Technology, Wuhan, China

Jun He

Institute of Semiconductors, Henan Academy of Sciences, Zhengzhou, China

Jun He

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This project was supervised and directed by J.H. and Y.L. Y.L. conceived this work. Y.L. and Y.X. designed the experiments. Y.L. and L.Y. conducted the device fabrication and electrical measurements. Y.L., H.W., Y.Y. and L.L. performed the material characterization. X.Z. conducted the density functional theory calculation. Y.X. performed the image recognition. All authors contributed to the discussion and analysis of the results. Y.L. wrote the manuscript.

Correspondence to Yesheng Li or Jun He.

The authors declare no competing interests.

Nature Electronics thanks Wenjing Jie, Huajun Sun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Li, Y., Xiong, Y., Zhang, X. et al. Memristors with analogue switching and high on/off ratios using a van der Waals metallic cathode. Nat Electron (2024). https://doi.org/10.1038/s41928-024-01269-y

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Received: 23 November 2023

Accepted: 27 September 2024

Published: 21 October 2024

DOI: https://doi.org/10.1038/s41928-024-01269-y

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