Electronic synapses made of layered two-dimensional materials


Neuromorphic computing systems, which use electronic synapses and neurons, could overcome the energy and throughput limitations of today’s computing architectures. However, electronic devices that can accurately emulate the short- and long-term plasticity learning rules of biological synapses remain limited. Here, we show that multilayer hexagonal boron nitride (h-BN) can be used as a resistive switching medium to fabricate high-performance electronic synapses. The devices can operate in a volatile or non-volatile regime, enabling the emulation of a range of synaptic-like behaviour, including both short- and long-term plasticity. The behaviour results from a resistive switching mechanism in the h-BN stack, based on the generation of boron vacancies that can be filled by metallic ions from the adjacent electrodes. The power consumption in standby and per transition can reach as low as 0.1 fW and 600 pW, respectively, and with switching times reaching less than 10 ns, demonstrating their potential for use in energy-efficient brain-like computing.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Fabrication of metal/h-BN/metal synapses.
Fig. 2: Dielectric breakdown in metal/h-BN/metal synapses.
Fig. 3: Volatile and non-volatile RS in metal/h-BN/metal synapses.
Fig. 4: In situ observation of volatile and non-volatile RS in h-BN.
Fig. 5: Dynamic response of metal/h-BN/metal synapses (type I).
Fig. 6: Dynamic response of metal/h-BN/metal synapses (type II).


  1. 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  Google Scholar 

  2. 2.

    Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22–29 (2018).

    Article  Google Scholar 

  3. 3.

    Hyvärinen, A. New approximations of differential entropy for independent component analysis and projection pursuit. In Advances in Neural Information Processing Systems 10, NIPS Proceedings ​273–279 (Neural Information Processing Systems Foundation, 1997).

  4. 4.

    Zucker, R. S. Short-term synaptic plasticity. Annu. Rev. Neurosci. 12, 13–31 (1989).

    Article  Google Scholar 

  5. 5.

    Abbott, L. F. & Nelson, S. B. Synaptic plasticity: taming the beast. Nat. Neurosci. 3, 1178–1183 (2000).

    Article  Google Scholar 

  6. 6.

    Widrow, B. & Lehr, M. A. 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation. Proc. IEEE 78, 1415–1442 (1990).

    Article  Google Scholar 

  7. 7.

    Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron. 1, 52–59 (2018).

    Article  Google Scholar 

  8. 8.

    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 

  9. 9.

    Ohno, T. et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (2011).

    Article  Google Scholar 

  10. 10.

    Kuzum, D., Yu, S. & Wong, H.-S. P. Synaptic electronics: materials, devices and applications. Nanotechnology 24, 382001 (2013).

    Article  Google Scholar 

  11. 11.

    Wu, H., Yao, P., Gao, B. & Qian, H. Multiplication on the edge. Nat. Electron. 1, 8–9 (2018).

    Article  Google Scholar 

  12. 12.

    Tsuruoka, T., Hasegawa, T., Terabe, K. & Aono, M. Conductance quantization and synaptic behavior in a Ta2O5-based atomic switch. Nanotechnology 23, 435705 (2012).

    Article  Google Scholar 

  13. 13.

    Chanthbouala, A. et al. A ferroelectric memristor. Nat. Mater. 11, 860–864 (2012).

    Article  Google Scholar 

  14. 14.

    Li, Y. et al. Activity-dependent synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems. Sci. Rep. 4, 4906 (2014).

    Article  Google Scholar 

  15. 15.

    Du, C., Ma, W., Chang, T., Sheridan, P. & Lu, W. D. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25, 4290–4299 (2015).

    Article  Google Scholar 

  16. 16.

    Berdan, R. et al. Emulating short-term synaptic dynamics with memristive devices. Sci. Rep. 6, 18639 (2016).

    Article  Google Scholar 

  17. 17.

    Boyn, S. et al. Learning through ferroelectric domain dynamics in solid-state synapses. Nat. Commun. 8, 14736 (2017).

    Article  Google Scholar 

  18. 18.

    Lashkare, S., Panwar, N., Kumbhare, P., Das, B. & Ganguly, U. PCMO-based RRAM and NPN bipolar selector as synapse for energy efficient STDP. IEEE Electron Device Lett. 38, 1212–1215 (2017).

    Article  Google Scholar 

  19. 19.

    van de Burgt, Y. et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017).

    Article  Google Scholar 

  20. 20.

    Cannon, R. C., O’Donnell, C. & Nolan, M. F. Stochastic ion channel gating in dendritic neurons: morphology dependence and probabilistic synaptic activation of dendritic spikes. PLoS Comput. Biol. 6, e1000886 (2010).

    MathSciNet  Article  Google Scholar 

  21. 21.

    Yu, S. et al. Stochastic learning in oxide binary synaptic device for neuromorphic computing. Front. Neurosci. 7, 186 (2013).

    Article  Google Scholar 

  22. 22.

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

    Article  Google Scholar 

  23. 23.

    Werner, T. et al. Experimental demonstration of short and long term synaptic plasticity using OxRAM multi k-bit arrays for reliable detection in highly noisy input data. 2016 IEEE Int. Electron Devices Meet. (IEDM) https://doi.org/10.1109/IEDM.2016.7838433 (2016).

  24. 24.

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

    Article  Google Scholar 

  25. 25.

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

    Article  Google Scholar 

  26. 26.

    Tian, H. et al. Anisotropic black phosphorus synaptic device for neuromorphic applications. Adv. Mater. 28, 4991–4997 (2016).

    Article  Google Scholar 

  27. 27.

    Tian, H. et al. Graphene dynamic synapse with modulatable plasticity. Nano Lett. 15, 8013–8019 (2015).

    Article  Google Scholar 

  28. 28.

    Tian, H. et al. A novel artificial synapse with dual modes using bilayer graphene as the bottom electrode. Nanoscale 9, 9275–9283 (2017).

    Article  Google Scholar 

  29. 29.

    Hui, F. et al. On the use of two dimensional hexagonal boron nitride as dielectric. Microelec. Eng. 163, 119–133 (2016).

    Article  Google Scholar 

  30. 30.

    Kim, K. K. et al. Synthesis and characterization of hexagonal boron nitride film as a dielectric layer for graphene devices. ACS Nano 6, 8583–8590 (2012).

    Article  Google Scholar 

  31. 31.

    Song, L. et al. Large scale growth and characterization of atomic hexagonal boron nitride layers. Nano Lett. 10, 3209–3215 (2010).

    Article  Google Scholar 

  32. 32.

    Lee, K. H. et al. Large-scale synthesis of high-quality hexagonal boron nitride nanosheets for large-area graphene electronics. Nano Lett. 12, 714–718 (2012).

    Article  Google Scholar 

  33. 33.

    Shi, Y. et al. Coexistence of volatile and non-volatile resistive switching in 2D h-BN based electronic synapses. 2017 IEEE Int. Electron Devices Meet. (IEDM) https://doi.org/10.1109/IEDM.2017.8268333 (2017).

  34. 34.

    Lanza, M. et al. Influence of the manufacturing process on the electrical properties of thin (<4nm) hafnium based high-k stacks observed with CAFM. Microelectron. Reliab. 47, 1424–1428 (2007).

    Article  Google Scholar 

  35. 35.

    Weinberg, Z. A. & Nguyen, T. N. The relation between positive charge and breakdown in metal-oxide-silicon structures. J. Appl. Phys. 61, 1947–1956 (1987).

    Article  Google Scholar 

  36. 36.

    Chen, Y. Y. et al. Tailoring switching and endurance/retention reliability characteristics of HfO2/Hf RRAM with Ti, Al, Si dopants. 2014 Symp. VLSI Tech. https://doi.org/10.1109/VLSIT.2014.6894403 (2014).

  37. 37.

    Belmonte, A. et al. A thermally stable and high-performance 90-nm Al2O3/Cu-based 1T1R CBRAM cell. IEEE Trans. Electron Dev. 60, 3690–3695 (2013).

    Article  Google Scholar 

  38. 38.

    Xiao, N. et al. Resistive random access memory cells with a bilayer TiO2/SiOx insulating stack for simultaneous filamentary and distributed resistive switching. Adv. Funct. Mater. 27, 1700384 (2017).

    Article  Google Scholar 

  39. 39.

    Tang, K. et al. Distinguishing oxygen vacancy electromigration and conductive filament formation in TiO2 resistance switching using liquid electrolyte contacts. Nano Lett. 17, 4390–4399 (2017).

    Article  Google Scholar 

  40. 40.

    Suñé, J. et al. On the breakdown statistics of very thin SiO2 films. Thin Solid Films 185, 347–362 (1990).

    Article  Google Scholar 

  41. 41.

    Uppal, H. J. Breakdown and degradation of ultrathin Hf-based (HfO2)x(SiO2)1–x gate oxide films. J. Vac. Sci. Technol. B 27, 443–447 (2009).

    Article  Google Scholar 

  42. 42.

    Lanza, M. et al. Recommended methods to study resistive switching devices. Adv. Electron. Mater. (in the press).

  43. 43.

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

    Article  Google Scholar 

  44. 44.

    Waser, R., Dittmann, R., Staikov, G. & Szot, K. Redox-based resistive switching memories—nanoionic mechanisms, prospects, and challenges. Adv. Mater. 21, 2632–2663 (2009).

    Article  Google Scholar 

  45. 45.

    Valov, I., Waser, R., Jameson, J. R. & Kozicki, M. N. Electrochemical metallization memories—fundamentals, applications, prospects. Nanotechnology 22, 254003 (2011).

    Article  Google Scholar 

  46. 46.

    Zobelli, A., Ewels, C. P., Gloter, A. & Seifert, G. Vacancy migration in hexagonal boron nitride. Phys. Rev. B 75, 094104 (2007).

    Article  Google Scholar 

  47. 47.

    Pan, F., Gao, S., Chen, C., Song, C. & Zeng, F. Recent progress in resistive random access memories: materials, switching mechanisms, and performance. Mater. Sci. Eng. R 83, 1–59 (2014).

    Article  Google Scholar 

  48. 48.

    Cortese, S., Trapatseli, M., Khiat, A., & Prodromakis, T. A TiO2-based volatile threshold switching selector device with 107 non linearity and sub 100 pA off current. 2016 Int. Symp. VLSI Tech. Syst. Appl. https://doi.org/10.1109/VLSI-TSA.2016.7480484 (2016).

  49. 49.

    Frammelsberger, W., Benstetter, G., Kiely, J. & Stamp, R. C-AFM-based thickness determination of thin and ultra-thin SiO2 films by use of different conductive-coated probe tips. Appl. Surf. Sci. 253, 3615–3626 (2007).

    Article  Google Scholar 

  50. 50.

    Jonscher, A. K. Dielectric relaxation in solids. J. Phys. D 32, R57–R70 (1999).

    Article  Google Scholar 

Download references


This work was supported by the member companies of the Non-Volatile Memory Technology Research Initiative (NMTRI) at Stanford University, the National Science Foundation EFRI 2-DARE EFRI: Energy-Efficient Electronics with Atomic Layers (E3AL) (award no. 1542883), the National Science Foundation of China (grants 61502326, 41550110223, 11661131002), the Jiangsu Government (grant BK20150343), and the Ministry of Finance of China (grant SX21400213). P. C. McIntyre and K. Tang (Stanford University) are acknowledged for support with ionic liquid experiments. Q. Liu and X. Zhang (IMECAS) are acknowledged for support with the STDP experiments. M. A. Villena and X. Jing are acknowledged for support with the SPICE simulation and mechanical exfoliation of h-BN, respectively.

Author information




M.L., Y.S., E.P. and H.-S.P.W. designed the experiments. Y.S., V.C. and F.H. grew the h-BN stacks. Y.S. and X.L. fabricated the electronic synapses using photolithography, and B.Y. fabricated the electronic synapses using electron-beam lithography. Y.S., X.L., B.Y, Z.Y. and F.Y. characterized the devices. Y.S., H.L., M.L. and H.-S.P.W. wrote the manuscript. All authors discussed the data and results.

Corresponding authors

Correspondence to H.-S. Philip Wong or 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 Figures 1–24 and Supplementary Table 1

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shi, Y., Liang, X., Yuan, B. et al. Electronic synapses made of layered two-dimensional materials. Nat Electron 1, 458–465 (2018). https://doi.org/10.1038/s41928-018-0118-9

Download citation

Further reading