Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

Nature Electronics volume 3, pages 638–645 (2020)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

References

  1. Li, M.-Y. et al. How 2D semiconductors could extend Moore’s law. Nature 567, 169–170 (2019).

    Article  Google Scholar 

  2. Akinwande, D. et al. Graphene and two-dimensional materials for silicon technology. Nature 573, 507–518 (2019).

    Article  Google Scholar 

  3. Lee, J. S. et al. Wafer-scale single-crystal hexagonal boron nitride film via self-collimated grain formation. Science 362, 817–821 (2018).

    Article  Google Scholar 

  4. Li, X. et al. Large-area synthesis of high-quality and uniform graphene films on copper foils. Science 324, 1312–1314 (2009).

    Article  Google Scholar 

  5. Lin, Z. et al. Solution-processable 2D semiconductors for high-performance large-area electronics. Nature 562, 254–258 (2018).

    Article  Google Scholar 

  6. Zhang, X. et al. Two-dimensional MoS2-enabled flexible rectenna for Wi-Fi-band wireless energy harvesting. Nature 566, 368–372 (2019).

    Article  Google Scholar 

  7. Cheng, C.-C. et al. First demonstration of 40-nm channel length top-gate WS2 pFET using channel area-selective CVD growth directly on SiOX/Si substrate. In Symposium on Very Large Scale Integration (VLSI) Technology Digest of Technical Papers T244–T245 (IEEE, 2019).

  8. Smets Q. et al. Ultra-scaled MOCVD MoS2 MOSFETs with 42 nm contact pitch and 250 µA/µm drain current. In International Electron Devices Meeting (IEDM) 23.2.1–23.2.4 (IEEE, 2019).

  9. Wang, C.-Y. et al. 2D layered materials for memristive and neuromorphic applications. Adv. Electron. Mater. 6, 1901107 (2020).

  10. Wang, M. et al. Robust memristors based on layered two-dimensional materials. Nat. Electron. 1, 130–136 (2018).

    Article  Google Scholar 

  11. Pan, C. 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 

  12. Shi, Y. et al. Electronic synapses made of layered two-dimensional materials. Nat. Electron. 1, 458–465 (2018).

    Article  Google Scholar 

  13. Yao, J. et al. Highly transparent nonvolatile resistive memory devices from silicon oxide and graphene. Nat. Commun. 3, 1101 (2012).

    Article  Google Scholar 

  14. Son, D. et al. Colloidal synthesis of uniform-sized molybdenum disulfide nanosheets for wafer-scale flexible nonvolatile memory. Adv. Mater. 28, 9326–9332 (2016).

    Article  Google Scholar 

  15. Ielmini, D. & Waser, R. (eds) Resistive Switching: from Fundamentals of Nanoionic Redox Processes to Memristive Device Applications (Wiley-VCH, 2016).

  16. Wong, H.-S. P. et al. Metal–oxide RRAM. Proc. IEEE 100, 1951–1970 (2012).

    Article  Google Scholar 

  17. Hui, F. et al. Graphene and related materials for resistive random access memories. Adv. Electron. Mater. 3, 1600195 (2017).

  18. Hui, F. & Lanza, M. Scanning probe microscopy for advanced nanoelectronics. Nat. Electron. 2, 221–229 (2019).

    Article  Google Scholar 

  19. Lanza, M. (ed.) Conductive Atomic Force Microscopy: Applications in Nanomaterials (Wiley-VCH, 2017).

  20. Ranjan, A. et al. CAFM based spectroscopy of stress-induced defects in HfO2 with experimental evidence of the clustering model and metastable vacancy defect state. In International Reliability Physics Symposium (IRPS) 7A.4.1–7A-4-7 (IEEE, 2016).

  21. Prezioso M. et al. Modeling and implementation of firing-rate neuromorphic-network classifiers with bilayer Pt/Al2O3/TiO2−x/Pt. In International Electron Devices Meeting (IEDM) 17.4.1–17.4.4 (IEEE, 2015).

  22. Adam, G. C. et al. 3-D memristor crossbars for analog and neuromorphic computing applications. IEEE Trans. Electron Devices 64, 312–318 (2017).

    Article  Google Scholar 

  23. Gaba, S., Sheridan, P., Zhou, J., Choi, S. & Lu, W. Stochastic memristive devices for computing and neuromorphic applications. Nanoscale 5, 5872–5878 (2013).

    Article  Google Scholar 

  24. Lanza, M. et al. Recommended methods to study resistive switching devices. Adv. Electron. Mater. 64, 1800143 (2018).

    Google Scholar 

  25. Sangwan, V. K. et al. Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide. Nature 554, 500–504 (2018).

    Article  Google Scholar 

  26. Yang, R. et al. Ternary content-addressable memory with MoS2 transistors for massively parallel data search. Nat. Electron. 2, 108–114 (2019).

    Article  Google Scholar 

  27. Bavandpour, M. et al. Mixed-signal neuromorphic inference accelerators: recent results and future prospects. In International Electron Device Meeting (IEDM) 20.4.1–20.4.4 (IEEE, 2018).

  28. Choi, S. et al. SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations. Nat. Mater. 17, 335–340 (2018).

    Article  Google Scholar 

  29. Merrikh Bayat, F. et al. High-performance mixed-signal neurocomputing with nanoscale floating-gate memory cells. IEEE Trans. Neural Netw. Learn. Syst. 29, 4782–4790 (2018).

    Article  Google Scholar 

  30. Fuller, E. J. et al. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364, 570–574 (2019).

    Article  Google Scholar 

  31. Ambrogio, S. et al. Equivalent-accuracy accelerated neural-network training using analogue memory. Nature 558, 60–64 (2018).

    Article  Google Scholar 

  32. Strukov, D. B. Tightening grip. Nat. Mater. 17, 293–295 (2018).

    Article  Google Scholar 

  33. Zhang, X. et al. An artificial neuron based on a threshold switching memristor. IEEE Electron Device Lett. 39, 308–311 (2018).

    Article  Google Scholar 

  34. Midya, R. et al. Artificial neural network (ANN) to spiking neural network (SNN) converters based on diffusive memristors. Adv. Electron. Mater. 5, 1900060 (2019).

    Article  Google Scholar 

  35. Lee, D. et al. Various threshold switching devices for integrate and fire neuron applications. Adv. Electron. Mater. 78, 1800866 (2019).

    Article  Google Scholar 

  36. Saleh, B. E. A. & Teich, M. C. Fundamentals of Photonics (Wiley-VCH, 2019).

  37. Stathopoulos, S. et al. Multibit memory operation of metal-oxide bi-layer memristors. Sci. Rep. 7, 17532 (2017).

    Article  Google Scholar 

  38. Berberan-Santos, M. N., Bodunov, E. N. & Valeur, B. Mathematical functions for the analysis of luminescence decays with underlying distributions 1. Kohlrausch decay function (stretched exponential). Chem. Phys. 315, 171–182 (2005).

    Article  Google Scholar 

  39. Sturman, B., Podivilov, E. & Gorkunov, M. Origin of stretched exponential relaxation for hopping-transport models. Phys. Rev. Lett. 91, 176602 (2003).

    Article  Google Scholar 

  40. Chang, T., Jo, S.-H. & Lu, W. Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano 5, 7669–7676 (2011).

    Article  Google Scholar 

  41. Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2017).

    Article  Google Scholar 

  42. Midya, R. et al. Anatomy of Ag/hafnia-based selectors with 1010 nonlinearity. Adv. Mater. 29, 1604457 (2017).

    Article  Google Scholar 

  43. Pi, S. et al. Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension. Nat. Nanotechnol. 14, 35–39 (2019).

    Article  Google Scholar 

  44. Alibart, F., Gao, L., Hoskins, B. & Strukov, D. B. High-precision tuning of state for memristive devices by adaptable variation-tolerant algorithm. Nanotechnology 23, 075201 (2012).

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Institute of Functional Nano and Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nanoscience & Technology, Soochow University, Suzhou, China

    Shaochuan Chen, Chandreswar Mahata, Bin Yuan, Xianhu Liang, Chao Wen & Mario Lanza

  2. Electrical and Computer Engineering Department, University of California at Santa Barbara, Santa Barbara, CA, USA

    Shaochuan Chen, Mohammad Reza Mahmoodi & Dmitri B. Strukov

  3. IMEC, Leuven, Belgium

    Yuanyuan Shi

  4. Materials Science and Engineering Department, Technion—Israel Institute of Technology, Haifa, Israel

    Fei Hui

  5. Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA

    Deji Akinwande

Authors
  1. Shaochuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Reza Mahmoodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuanyuan Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chandreswar Mahata
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bin Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xianhu Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chao Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fei Hui
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Deji Akinwande
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Dmitri B. Strukov
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Mario Lanza
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-020-00473-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing