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:

A van der Waals interfacial junction transistor for reconfigurable fuzzy logic hardware

Nature Electronics volume 7, pages 876–884 (2024)Cite this article

Subjects

Abstract

Edge devices face challenges when implementing deep neural networks due to constraints on their computational resources and power consumption. Fuzzy logic systems can potentially provide more efficient edge implementations due to their compactness and capacity to manage uncertain data. However, their hardware realization remains difficult, primarily because implementing reconfigurable membership function generators using conventional technologies requires high circuit complexity and power consumption. Here we report a multigate van der Waals interfacial junction transistor based on a molybdenum disulfide/graphene heterostructure that can generate tunable Gaussian-like and π-shaped membership functions. By integrating these generators with peripheral circuits, we create a reconfigurable fuzzy controller hardware capable of nonlinear system control. This fuzzy logic system can also be integrated with a few-layer convolution neural network to form a fuzzy neural network with enhanced performance in image segmentation.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Device structure, PL spectra and electrical characteristics of a vdW-IJT.
Fig. 2: Device structure and highly tunable electrical characteristics of a multigate vdW-IJT.
Fig. 3: Hardware implementation of a fuzzy PID controller.
Fig. 4: FNN for image segmentation.
Fig. 5: Comparison of reconfigurable MFGs between this work and traditional CMOS approaches.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available via the Harvard Dataverse repository at https://doi.org/10.7910/DVN/VBOVVW.

Code availability

The code used in this study is available via GitHub at https://github.com/hexu2333/Unet-with-Fuzzy-Layer.

References

  1. Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P. & Nikolopoulos, D. S. Challenges and opportunities in edge computing. In 2016 IEEE International Conference on Smart Cloud (SmartCloud) 20–26 (IEEE, 2016).

  2. Shi, W., Cao, J., Zhang, Q., Li, Y. & Xu, L. Edge computing: vision and challenges. IEEE Internet Things J. 3, 637–646 (2016).

    Article  Google Scholar 

  3. Mendel, J. M. Fuzzy logic systems for engineering: a tutorial. Proc. IEEE 83, 345–377 (1995).

    Article  Google Scholar 

  4. van der Wal, A. J. Application of fuzzy logic control in industry. Fuzzy Sets Syst. 74, 33–41 (1995).

    Article  Google Scholar 

  5. Hitzler, P. & Sarker, M. K. Neuro-Symbolic Artificial Intelligence: The State of the Art (IOS Press, 2022).

  6. Baturone, I., Barriga, A., Jimenez-Fernandez, C., Lopez, D. R. & Sanchez-Solano, S. Microelectronic Design of Fuzzy Logic-Based Systems (CRC Press, 2018).

  7. Peyravi, H., Khoei, A. & Hadidi, K. Design of an analog CMOS fuzzy logic controller chip. Fuzzy Sets Syst. 132, 245–260 (2002).

    Article  MathSciNet  Google Scholar 

  8. Wang, W.-z. & Jin, D.-m. Neuro-fuzzy system with high-speed low-power analog blocks. Fuzzy Sets Syst. 157, 2974–2982 (2006).

    Article  MathSciNet  Google Scholar 

  9. Baradaranrezaeii, A., Zarei, M., Khalilzadegan, A., Khoei, A. & Hadidi, K. A CMOS reference-less membership function generator. In 2011 19th Iranian Conference on Electrical Engineering 1–6 (IEEE, 2011).

  10. Khalilzadegan, A., Khoei, A. & Hadidi, K. Circuit implementation of a fully programmable and continuously slope tunable triangular/trapezoidal membership function generator. Analog Integr. Circuits Signal Process. 71, 561–570 (2012).

    Article  Google Scholar 

  11. Yaghmourali, Y. V., Fathi, A., Hassanzadazar, M., Khoei, A. & Hadidi, K. A low-power, fully programmable membership function generator using both transconductance and current modes. Fuzzy Sets Syst. 337, 128–142 (2018).

    Article  MathSciNet  Google Scholar 

  12. Jooq, M. K. Q., Behbahani, F., Al-Shidaifat, A., Khan, S. R. & Song, H. A high-performance and ultra-efficient fully programmable fuzzy membership function generator using FinFET technology for image enhancement. Int. J. Electron. Commun. 163, 154598 (2023).

    Article  Google Scholar 

  13. Li, H. et al. Interfacial interactions in van der Waals heterostructures of MoS2 and graphene. ACS Nano 11, 11714–11723 (2017).

    Article  Google Scholar 

  14. Johnson, M. A. & Moradi, M. H. PID Control (Springer, 2005).

  15. Buckley, J. J. & Hayashi, Y. J. Fuzzy neural networks: a survey. Fuzzy Sets Syst. 66, 1–13 (1994).

    Article  MathSciNet  Google Scholar 

  16. de Campos Souza, P. V. Fuzzy neural networks and neuro-fuzzy networks: a review the main techniques and applications used in the literature. Appl. Soft Comput. 92, 106275 (2020).

    Article  Google Scholar 

  17. Mak, K. F. et al. Tightly bound trions in monolayer MoS2. Nat. Mater. 12, 207–211 (2013).

    Article  Google Scholar 

  18. Craciun, M. et al. Trilayer graphene is a semimetal with a gate-tunable band overlap. Nat. Nanotechnol. 4, 383–388 (2009).

    Article  Google Scholar 

  19. Shih, C.-J. et al. Tuning on–off current ratio and field-effect mobility in a MoS2–graphene heterostructure via Schottky barrier modulation. ACS Nano 8, 5790–5798 (2014).

    Article  Google Scholar 

  20. Lee, C.-H. et al. Atomically thin p–n junctions with van der Waals heterointerfaces. Nat. Nanotechnol. 9, 676–681 (2014).

    Article  Google Scholar 

  21. Zhang, X. et al. Near-ideal van der Waals rectifiers based on all-two-dimensional Schottky junctions. Nat. Commun. 12, 1522 (2021).

    Article  Google Scholar 

  22. Lee, H. S. et al. Metal semiconductor field-effect transistor with MoS2/conducting NiOx van der Waals Schottky interface for intrinsic high mobility and photoswitching speed. ACS Nano 9, 8312–8320 (2015).

    Article  Google Scholar 

  23. Shin, H. G. et al. Vertical and in-plane current devices using NbS2/n-MoS2 van der Waals Schottky junction and graphene contact. Nano Lett. 18, 1937–1945 (2018).

    Article  Google Scholar 

  24. Wang, J. et al. Transferred metal gate to 2D semiconductors for sub-1 V operation and near ideal subthreshold slope. Sci. Adv. 7, eabf8744 (2021).

    Article  Google Scholar 

  25. Fu, J. et al. Photo‐driven semimetal–semiconductor field‐effect transistors. Adv. Opt. Mater. 11, 2201983 (2023).

    Article  Google Scholar 

  26. Parker, A. E. & Skellern, D. J. A realistic large-signal MESFET model for SPICE. IEEE Trans. Microw. Theory Tech. 45, 1563–1571 (1997).

    Article  Google Scholar 

  27. Ning, T. H. & Cai, J. On the performance and scaling of symmetric lateral bipolar transistors on SOI. IEEE J. Electron Devices Soc. 1, 21–27 (2013).

    Article  Google Scholar 

  28. Mishra, S., Singh, V. K. & Pal, B. B. Effect of radiation and surface recombination on the characteristics of an ion-implanted GaAs MESFET. IEEE Trans. Electron Devices 37, 2–10 (1990).

    Article  Google Scholar 

  29. Hájek, P. Metamathematics of Fuzzy Logic Vol. 4 (Springer, 2013).

  30. Beck, M. E. et al. Spiking neurons from tunable Gaussian heterojunction transistors. Nat. Commun. 11, 1565 (2020).

    Article  Google Scholar 

  31. Yan, X. et al. Reconfigurable mixed-kernel heterojunction transistors for personalized support vector machine classification. Nat. Electron. 6, 862–869 (2023).

    Article  Google Scholar 

  32. Tang, K.-S., Man, K. F., Chen, G. & Kwong, S. An optimal fuzzy PID controller. IEEE Trans. Ind. Electron. 48, 757–765 (2001).

    Article  Google Scholar 

  33. MacVicar-Whelan, P. Fuzzy sets for man–machine interaction. Int. J. Man Mach. Stud. 8, 687–697 (1976).

    Article  Google Scholar 

  34. Mamdani, E. H. & Assilian, S. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7, 1–13 (1975).

    Article  Google Scholar 

  35. Guo, S., Peters, L. & Surmann, H. Design and application of an analog fuzzy logic controller. IEEE Trans. Fuzzy Syst. 4, 429–438 (1996).

    Article  Google Scholar 

  36. Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Part III (eds Navab, N. et al.) 234–241 (Springer, 2015).

  37. The Cityscapes Dataset; https://www.cityscapes-dataset.com/

  38. Cordts, M. et al. The Cityscapes dataset for semantic urban scene understanding. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3213–3223 (IEEE, 2016).

  39. Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).

    Article  Google Scholar 

  40. Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020).

    Article  Google Scholar 

  41. Kim, M.-K., Kim, I.-J. & Lee, J.-S. CMOS-compatible compute-in-memory accelerators based on integrated ferroelectric synaptic arrays for convolution neural networks. Sci. Adv. 8, eabm8537 (2022).

    Article  Google Scholar 

  42. Dorzhigulov, A., Choubey, B. & James, A. P. Current controlled neuro-fuzzy membership function generation. In 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS) 929–932 (IEEE, 2019).

  43. Azimi, S. & Miar-Naimi, H. Designing programmable current-mode Gaussian and bell-shaped membership function. Analog Integr. Circuits Signal Process. 102, 323–330 (2020).

    Article  Google Scholar 

  44. Khaneshan, T. M., Nematzadeh, M., Khoei, A. & Hadidi, K. An analog reconfigurable Gaussian-shaped membership function generator using current-mode techniques. In 20th Iranian Conference on Electrical Engineering (ICEE2012) 145–149 (IEEE, 2012).

  45. Lin, K.-J., Cheng, C.-J., Chiu, S.-F. & Su, H.-C. CMOS current-mode implementation of fractional-power functions. Circuits Syst. Signal Process. 31, 61–75 (2012).

    Article  MathSciNet  Google Scholar 

  46. Saatlo, A. N. & Ozoguz, S. CMOS implementation of scalable Morlet wavelet for application in signal processing. In 2015 38th International Conference on Telecommunications and Signal Processing (TSP) 1–4 (IEEE, 2015).

  47. Khayatzadeh, R. & Yelten, M. B. A novel multiple membership function generator for fuzzy logic systems. In 2018 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD) 101–104 (IEEE, 2018).

  48. Bozorgmehr, A., Jooq, M. K. Q., Moaiyeri, M. H., Navi, K. & Bagherzadeh, N. A high-performance fully programmable membership function generator based on 10 nm gate-all-around CNTFETs. Int. J. Electron. Commun. 123, 153293 (2020).

    Article  Google Scholar 

  49. Ghasemian, H., Karami, S., Abiri, E. & Salehi, M. R. Design of a low power analog and multi-shaped fully programmable twin-cell membership function generator circuit in 65 nm CMOS technology. Circuits Syst. Signal Process. 40, 2–21 (2021).

    Article  Google Scholar 

  50. Yan, X. et al. Reconfigurable stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine. Nat. Commun. 12, 5710 (2021).

    Article  Google Scholar 

Download references

Acknowledgements

X.Y., J.H.Q. and M.C.H. acknowledge support from the US Department of Energy Office of Science ASCR and BES Microelectronics Threadwork Program (contract number DE-AC02-06CH11357) and the US National Science Foundation EFRI BRAID Program (contract number EFMA-2317974). N.Y. and J.G. acknowledge support from the US National Science Foundation (contract numbers 2203625 and 2007200).

Author information

Author notes

  1. These authors contributed equally: Hefei Liu, Jiangbin Wu, Jiahui Ma.

Authors and Affiliations

  1. Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA

    Hefei Liu, Jiahui Ma, Hongming Zhang & Ting-Hao Hsu

  2. State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China

    Jiangbin Wu

  3. Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA

    Xiaodong Yan, Justin H. Qian & Mark C. Hersam

  4. Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA

    Ning Yang & Jing Guo

  5. Joint Institute, Shanghai Jiao Tong University, Shanghai, China

    Xu He

  6. Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China

    Yangu He & Han Wang

  7. Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA

    Mark C. Hersam

  8. Department of Chemistry, Northwestern University, Evanston, IL, USA

    Mark C. Hersam

Authors
  1. Hefei Liu
    View author publications

    Search author on:PubMed Google Scholar

  2. Jiangbin Wu
    View author publications

    Search author on:PubMed Google Scholar

  3. Jiahui Ma
    View author publications

    Search author on:PubMed Google Scholar

  4. Xiaodong Yan
    View author publications

    Search author on:PubMed Google Scholar

  5. Ning Yang
    View author publications

    Search author on:PubMed Google Scholar

  6. Xu He
    View author publications

    Search author on:PubMed Google Scholar

  7. Yangu He
    View author publications

    Search author on:PubMed Google Scholar

  8. Hongming Zhang
    View author publications

    Search author on:PubMed Google Scholar

  9. Ting-Hao Hsu
    View author publications

    Search author on:PubMed Google Scholar

  10. Justin H. Qian
    View author publications

    Search author on:PubMed Google Scholar

  11. Jing Guo
    View author publications

    Search author on:PubMed Google Scholar

  12. Mark C. Hersam
    View author publications

    Search author on:PubMed Google Scholar

  13. Han Wang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

H.W., H.L. and J.W. conceived the project concept. H.W. supervised the entire project. H.W., H.L., J.W., J.M., X.Y. and M.C.H. designed the experiments and simulations. H.L., J.W., J.M., H.Z. and T.-H.H. fabricated the devices. H.L., J.W. and J.M. carried out the electrical measurements. N.Y. and J.G. carried out the device simulation. H.L., J.W., X.H. and Y.H. designed and carried out the FNN modelling. H.W., M.C.H., X.Y. and J.H.Q. participated in the experiments and data analysis. H.L., J.W. and H.W. co-wrote the paper. All authors discussed the results and provided inputs on the paper at all stages.

Corresponding authors

Correspondence to Mark C. Hersam or Han Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Electronics thanks Tao Liu and the other, anonymous, reviewer(s) 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. 1–14, Notes 1–4 and Table 1.

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

Liu, H., Wu, J., Ma, J. et al. A van der Waals interfacial junction transistor for reconfigurable fuzzy logic hardware. Nat Electron 7, 876–884 (2024). https://doi.org/10.1038/s41928-024-01256-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41928-024-01256-3

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