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:

Atomically thin optomemristive feedback neurons

A Publisher Correction to this article was published on 07 July 2023

This article has been updated

Abstract

Cognitive functions such as learning in mammalian brains have been attributed to the presence of neuronal circuits with feed-forward and feedback topologies. Such networks have interactions within and between neurons that provide excitory and inhibitory modulation effects. In neuromorphic computing, neurons that combine and broadcast both excitory and inhibitory signals using one nanoscale device are still an elusive goal. Here we introduce a type-II, two-dimensional heterojunction-based optomemristive neuron, using a stack of MoS2, WS2 and graphene that demonstrates both of these effects via optoelectronic charge-trapping mechanisms. We show that such neurons provide a nonlinear and rectified integration of information, that can be optically broadcast. Such a neuron has applications in machine learning, particularly in winner-take-all networks. We then apply such networks to simulations to establish unsupervised competitive learning for data partitioning, as well as cooperative learning in solving combinatorial optimization problems.

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

Access options

Buy this article

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

Fig. 1: Competitive neuron concept.
Fig. 2: Type-II heterojunction neuron.
Fig. 3: Excitory characteristics.
Fig. 4: Excitory and inhibitory characteristics.
Fig. 5: Winner-take-all learning.

Similar content being viewed by others

Data availability

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

Change history

References

  1. Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).

    Article  CAS  Google Scholar 

  2. M, D., N, S., T, L. & G, C. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018).

    Article  Google Scholar 

  3. Tuma, T., Pantazi, A., Le Gallo, M., Sebastian, A. & Eleftheriou, E. Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693–699 (2016).

    Article  CAS  Google Scholar 

  4. Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019).

    Article  CAS  Google Scholar 

  5. Kumar, S., Williams, R. S. & Wang, Z. Third-order nanocircuit elements for neuromorphic engineering. Nature 585, 518–523 (2020).

    Article  CAS  Google Scholar 

  6. Yi, W. et al. Biological plausibility and stochasticity in scalable VO2 active memristor neurons. Nat. Commun. 9, 4661 (2018).

    Article  Google Scholar 

  7. Hassan, N. et al. Magnetic domain wall neuron with lateral inhibition. J. Appl. Phys. 124, 152127 (2018).

    Article  Google Scholar 

  8. Kandel, E. R. et al. Principles of Neural Science Vol. 4 (McGraw-Hill, 2000).

  9. Yin, H. in Computational Intelligence: a Compendium (eds Fulcher, J. & Jain, L. C.) 715–762 (Springer, 2008).

  10. Maass, W. On the computational power of winner-take-all. Neural Comput. 12, 2519–2535 (2000).

    Article  CAS  Google Scholar 

  11. Maass, W. Neural computation with winner-take-all as the only nonlinear operation. Adv. Neural Inf. Process. Syst. 12, 293–299 (2000).

    Google Scholar 

  12. Lazzaro, J., Ryckebusch, S., Mahowald, M. A. & Mead, C. A. Winner-take-all networks of O(n) complexity. Adv. Neural Inf. Process. Syst. 1, 703–711 (1988).

  13. Kaski, S. & Kohonen, T. Winner-take-all networks for physiological models of competitive learning. Neural Netw. 7, 973–984 (1994).

    Article  Google Scholar 

  14. Gerstner, W., Lehmann, M., Liakoni, V., Corneil, D. & Brea, J. Eligibility traces and plasticity on behavioral time scales: experimental support of neohebbian three-factor learning rules. Front. Neural Circuits 12, 53 (2018).

    Article  Google Scholar 

  15. Ferguson, K. A. & Cardin, J. A. Mechanisms underlying gain modulation in the cortex. Nat. Rev. Neurosci. 21, 80–92 (2020).

    Article  CAS  Google Scholar 

  16. Kreiser, R., Moraitis, T., Sandamirskaya, Y. & Indiveri, G. On-chip unsupervised learning in winner-take-all networks of spiking neurons. In 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) 1–4 (IEEE, 2017).

  17. Hsu, D., Figueroa, M. & Diorio, C. Competitive learning with floating-gate circuits. IEEE Trans. Neural Netw. 13, 732–744 (2002).

    Article  CAS  Google Scholar 

  18. Diehl, P. U. & Cook, M. Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9, 99 (2015).

    Article  Google Scholar 

  19. Ebong, I. E. & Mazumder, P. CMOS and memristor-based neural network design for position detection. Proc. IEEE 100, 2050–2060 (2011).

    Article  Google Scholar 

  20. Srivastava, R. K., Masci, J., Kazerounian, S., Gomez, F. J. & Schmidhuber, J. Compete to compute. In Advances in Neural Information Processing Systems 26 (NIPS 2013), 2310–2318 (Citeseer, 2013).

  21. Oster, M., Douglas, R. & Liu, S.-C. Computation with spikes in a winner-take-all network. Neural Comput. 21, 2437–2465 (2009).

    Article  Google Scholar 

  22. Fenno, L., Yizhar, O. & Deisseroth, K. The development and application of optogenetics. Ann. Rev. Neurosci. 34, 389–412 (2011).

    Article  CAS  Google Scholar 

  23. Gradinaru, V. et al. Molecular and cellular approaches for diversifying and extending optogenetics. Cell 141, 154–165 (2010).

    Article  CAS  Google Scholar 

  24. Sarwat, S. G., Moraitis, T., Wright, C. D. & Bhaskaran, H. Chalcogenide optomemristors for multi-factor neuromorphic computation. Nat. Commun. 13, 2247 (2022).

    Article  CAS  Google Scholar 

  25. Wang, Q. et al. Nonvolatile infrared memory in MoS2/PbS van der Waals heterostructures. Sci. Adv. 4, eaap7916 (2018).

    Article  Google Scholar 

  26. Xiang, D. et al. Two-dimensional multibit optoelectronic memory with broadband spectrum distinction. Natu. Commun. 9, 2966 (2018).

    Article  Google Scholar 

  27. Tran, M. D. et al. Two-terminal multibit optical memory via van der Waals heterostructure. Adv. Mater. 31, 1807075 (2019).

    Article  Google Scholar 

  28. Lee, J. et al. Monolayer optical memory cells based on artificial trap-mediated charge storage and release. Nat. Commun. 8, 14734 (2017).

    Article  CAS  Google Scholar 

  29. Sze, S. M., Li, Y. & Ng, K. K. Physics of Semiconductor Devices (John Wiley & Sons, 2021).

  30. Amit, I. et al. Role of charge traps in the performance of atomically thin transistors. Adv. Mater. 29, 1605598 (2017).

    Article  Google Scholar 

  31. Kim, S. Y., Yang, H. I. & Choi, W. Photoluminescence quenching in monolayer transition metal dichalcogenides by Al2O3 encapsulation. Appl. Phys. Lett. 113, 133104 (2018).

    Article  Google Scholar 

  32. Li, Z., Wang, W., Greenham, N. C. & McNeill, C. R. Influence of nanoparticle shape on charge transport and recombination in polymer/nanocrystal solar cells. Phys. Chem. Chem. Phys. 16, 25684–25693 (2014).

    Article  CAS  Google Scholar 

  33. Carpenter, G. A. & Grossberg, S. A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput. Vis. Graph. Image Process. 37, 54–115 (1987).

    Article  Google Scholar 

  34. Kohonen, T. Essentials of the self-organizing map. Neural Netw. 37, 52–65 (2013).

    Article  Google Scholar 

  35. Kohonen, T. The self-organizing map. Proc. IEEE 78, 1464–1480 (1990).

    Article  Google Scholar 

  36. Oh, S. et al. Energy-efficient Mott activation neuron for full-hardware implementation of neural networks. Nat. Nanotechnol. 16, 680–687 (2021).

    Article  CAS  Google Scholar 

  37. Meng, W. et al. Three-dimensional monolithic micro-led display driven by atomically thin transistor matrix. Nat. Nanotechnol. 16, 1231–1236 (2021).

    Article  CAS  Google Scholar 

  38. Hwangbo, S., Hu, L., Hoang, A. T., Choi, J. Y. & Ahn, J.-H. Wafer-scale monolithic integration of full-colour micro-led display using MoS2 transistor. Nat. Nanotechnol. 17, 500–506 (2022).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We acknowledge funding for this work from the European Union’s Horizon 2020 Research and Innovation Programme (HyBrain project no. 101046878 and Fun-COMP project no. 780848); from the Engineering and Physical Sciences Research Council through grant numbers EP/R001677/1, EP/M015173/1 and EP/J018694/1; and from the European Research Council through grant no. 725258.

Author information

Authors and Affiliations

Authors

Contributions

S.G.S. and Y.Z. carried out the experiments and analysis. Y.Z. fabricated the devices. S.G.S. conceptualized and performed the device and machine learning simulations. S.G.S, Y.Z., J.W. and H.B. discussed the data and wrote the manuscript. J.W. and H.B. supervised the research.

Corresponding authors

Correspondence to Ghazi Sarwat Syed or Harish Bhaskaran.

Ethics declarations

Competing interests

H.B. is a founder of and holds shares in Salience Labs Ltd. The other authors declare no competing interests.

Peer review

Peer review information

Nature Nanotechnology thanks Chao Jiang and Suhas Kumar for their contribution to the peer review of this work.

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. 1–18 including tables in Figs. 10 and 14, and Discussion.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Syed, G.S., Zhou, Y., Warner, J. et al. Atomically thin optomemristive feedback neurons. Nat. Nanotechnol. 18, 1036–1043 (2023). https://doi.org/10.1038/s41565-023-01391-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41565-023-01391-6

This article is cited by

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