Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural networks

Abstract

Two-dimensional materials could play an important role in beyond-CMOS (complementary metal–oxide–semiconductor) electronics, and the development of memristors for information storage and neuromorphic computing using such materials is of particular interest. However, the creation of high-density electronic circuits for complex applications is limited due to low device yield and high device-to-device variability. Here, we show that high-density memristive crossbar arrays can be fabricated using hexagonal boron nitride as the resistive switching material, and used to model an artificial neural network for image recognition. The multilayer hexagonal boron nitride is deposited using chemical vapour deposition, and the arrays exhibit a high yield (98%), low cycle-to-cycle variability (1.53%) and low device-to-device variability (5.74%). The devices exhibit different switching mechanisms depending on the electrode material used (gold for bipolar switching and silver for threshold switching), as well as characteristics (such as large dynamic range and zeptojoule-order switching energies) that make them suited for application in neuromorphic circuits.

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Fig. 1: Material characterization and crossbar array fabrication.
Fig. 2: Bipolar RS in h-BN memristors and their application in neuromorphic computing.
Fig. 3: Threshold-type RS behaviour in h-BN memristors.
Fig. 4: RS characteristics of nanoscale crossbar and nanodot memristors.

Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work has been supported by the Ministry of Science and Technology of China (grant no. 2018YFE0100800), the National Natural Science Foundation of China (grants nos. 11661131002 and 61874075), the Ministry of Finance of China (grant no. SX21400213), the 111 Project from the State Administration of Foreign Experts Affairs of China, the Collaborative Innovation Centre of Suzhou Nano Science and Technology, the Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices and the Priority Academic Program Development of Jiangsu Higher Education Institutions. D.A. acknowledges a PECASE/ARO award. The authors acknowledge H. Nili and G. Wirth for technical advice on the electrical characterization, and Park Systems for allowing us to carry out CAFM experiments in their laboratories.

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Contributions

M.L. conceived the idea and designed the experiments. S.C., B.Y. and X.L. fabricated the memristor crossbar arrays. S.C., B.Y., X.L., Y.S., C.W. and F.H. characterized the memristor crossbar arrays. C.M. fabricated and characterized the nanodot memristors. M.R.M. and D.B.S. developed the neural network simulation. M.L., S.C., D.A. and D.B.S. wrote the manuscript. All authors discussed the data and revised the text.

Corresponding author

Correspondence to Mario Lanza.

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Supplementary Information

Supplementary Figs. 1–34, Tables 1 and 2, discussion and refs. 1–62.

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Chen, S., Mahmoodi, M.R., Shi, Y. 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). https://doi.org/10.1038/s41928-020-00473-w

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