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Two-terminal floating-gate transistors with a low-power memristive operation mode for analogue neuromorphic computing

Abstract

Metal–oxide memristive integrated technologies for analogue neuromorphic computing have undergone notable developments in the past decade, but are still not mature enough for very large-scale integration with complementary metal–oxide–semiconductor (CMOS) processes. Although non-volatile floating-gate synapse transistors are a more advanced technology embedded within CMOS processes, their performance as analogue resistive memories remains limited. Here, we report a low-power, two-terminal floating-gate transistor fabricated using standard single-poly technology in a commercial 180 nm CMOS process. Our device, which is integrated with a readout transistor, can operate in an energy-efficient subthreshold memristive mode. At the same time, it is linearized for small-signal changes with a two-orders-of-magnitude resistance dynamic range. Our device can be precisely tuned using optimized switching voltages and times, and can achieve 65 distinct resistive levels and ten-year analogue data retention. We experimentally demonstrate the feasibility of a selector-free integrated memristive array in basic neuromorphic applications, including spike-time-dependent plasticity, vector-matrix multiplication, associative memory and classification training.

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Fig. 1: Y-flash non-volatile memory device.
Fig. 2: Y-flash memristive device.
Fig. 3: Y-flash memristive synapses.
Fig. 4: Associative memory of a Y-flash memristive neural network.
Fig. 5: Training and classification of a Y-flash memristive neural network.

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.

Code availability

The computer codes used in this study are available within this paper and its Supplementary Information files.

References

  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.

    Hasler, J. & Marr, B. Finding a roadmap to achieve large neuromorphic hardware systems. Front. Neurosci. 7, 118 (2013).

    Article  Google Scholar 

  3. 3.

    Benjamin, B. V. et al. Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102, 699–716 (2014).

    Article  Google Scholar 

  4. 4.

    Furber, S. B., Galluppi, F., Temple, S. & Plana, S. The SpiNNaker project. Proc. IEEE 102, 652–665 (2014).

    Article  Google Scholar 

  5. 5.

    Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 73 (2011).

    Google Scholar 

  6. 6.

    Likharev, K. K. CrossNets: neuromorphic hybrid CMOS/nanoelectronic networks. Sci. Adv. Mater. 3, 322–331 (2011).

    Article  Google Scholar 

  7. 7.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  Google Scholar 

  8. 8.

    Diorio, C., Hasler, P., Minch, A. & Mead, C. Neuromorphic Systems Engineering: Neural Networks in Silicon Ch. 14 (Springer, 1998).

  9. 9.

    Diorio, C., Hasler, P., Minch, A. & Mead, C. A single-transistor silicon synapse. IEEE Trans. Electron. Dev. 43, 1972–1980 (1996).

    Article  Google Scholar 

  10. 10.

    Hasler, P., Minch, B. A. & Diorio, C. Adaptive circuits using pFET floating-gate devices. In Proceedings of the 20th Anniversary Conference on Advanced Research in VLSI (ARVLSI) 215–229 (IEEE, 1999).

  11. 11.

    Hasler, P., Diorio, C., Minch, B. A. & Mead, C. Single transistor learning synapses. In Proceedings of the 7th International Conference on Neural Information Processing Systems (NIPS) 817–824 (ACM, 1994).

  12. 12.

    Hasler, P., Minch, B. A. & Diorio, C. An autozeroing floating-gate amplifier. IEEE Trans. Circ. Syst. II 48, 74–82 (2001).

    Article  Google Scholar 

  13. 13.

    Ramakrishnan, S., Hasler, P. & Gordon, C. Floating gate synapses with spike time dependent plasticity. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS) 369–372 (IEEE, 2010).

  14. 14.

    Wong, H. S. P. & Salahuddin, S. Memory leads the way to better computing. Nat. Nanotechnol. 10, 191–194 (2015).

    Article  Google Scholar 

  15. 15.

    Waser, R. & Aono, M. Nanoionics-based resistive switching memories. Nat. Mater. 6, 833–840 (2007).

    Article  Google Scholar 

  16. 16.

    Chua, L. O. Memristor—the missing circuit element. IEEE Trans. Circuit Theory 18, 507–519 (1971).

    Article  Google Scholar 

  17. 17.

    Chua, L. O. & Kang, S. M. Memristive devices and systems. Proc. IEEE 64, 209–223 (1976).

    MathSciNet  Article  Google Scholar 

  18. 18.

    Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).

    Article  Google Scholar 

  19. 19.

    Jo, S. H. et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10, 1297–1301 (2010).

    Article  Google Scholar 

  20. 20.

    Wang, Z. et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 1, 137–145 (2018).

    Article  Google Scholar 

  21. 21.

    Xia, Q. & Yang, J. J. Memristive crossbar arrays for brain-inspired computing. Nat. Mater. 18, 309–323 (2019).

    Article  Google Scholar 

  22. 22.

    Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal–oxide memristors. Nature 521, 61–64 (2015).

    Article  Google Scholar 

  23. 23.

    Merrikh Bayat, F. et al. Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits. Nat. Commun. 9, 2331 (2018).

    Article  Google Scholar 

  24. 24.

    Adam, G. C., Khiat, A. & Prodromakis, T. Challenges hindering memristive neuromorphic hardware from going mainstream. Nat. Commun. 9, 5267 (2018).

    Article  Google Scholar 

  25. 25.

    Niu, D., Chen, Y., Xu, C. & Xie, Y. Impact of process variations on emerging memristor. In Proceedings of the 47th Design Automation Conference (DAC) 877–882 (IEEE, 2010).

  26. 26.

    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 

  27. 27.

    Pouyan, P., Amat, E. & Rubio, A. Reliability challenges in design of memristive memories. In Proceedings of the 5th European Workshop on CMOS Variability (VARI) 1–6 (IEEE, 2014).

  28. 28.

    Indiveri, G. et al. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology 24, 384010 (2013).

    Article  Google Scholar 

  29. 29.

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

    Article  Google Scholar 

  30. 30.

    Merrikh Bayat, F. et al. Redesigning commercial floating-gate memory for analog computing applications. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS) 1921–1924 (IEEE, 2015).

  31. 31.

    Merrikh Bayat, F. et al. Model-based high-precision tuning of NOR flash memory cells for analog computing applications. In Proceedings of the Device Research Conference (DRC) 1–2 (IEEE, 2016).

  32. 32.

    Guo, X. et al. Temperature-insensitive analog vector-by-matrix multiplier based on 55 nm NOR flash memory cells. In Proceedings of the IEEE Custom Integrated Circuits Conference (CICC) 1–4 (IEEE, 2017).

  33. 33.

    Guo, X. et al. Fast, energy-efficient, robust, and reproducible mixed-signal neuromorphic classifier based on embedded NOR flash memory technology. In Proceedings of the International Electron Devices Meeting (IEDM) 6.5.1–6.5.4 (IEEE, 2017).

  34. 34.

    Ziegler, M. et al. Memristive operation mode of floating gate transistors: a two-terminal MemFlash-cell. Appl. Phys. Lett. 101, 263504 (2012).

    Article  Google Scholar 

  35. 35.

    Ziegler, M. & Kohlstedt, H. Mimic synaptic behavior with a single floating gate transistor: a MemFlash synapse. J. Appl. Phys. 114, 194506 (2013).

    Article  Google Scholar 

  36. 36.

    Himmel, N. et al. Memristive device based on a depletion-type SONOS field effect transistor. Semicond. Sci. Technol. 32.6, 06LT01 (2017).

    Article  Google Scholar 

  37. 37.

    Winterfeld, H. et al. Technology and electrical characterization of MemFlash cells for neuromorphic applications. J. Appl. Phys. 51, 324003 (2018).

    Google Scholar 

  38. 38.

    Roizin, Y. & Pikhay, E. Memristor using parallel asymmetrical transistors having shared floating gate and diode. US patent 9,514,818 (2016).

  39. 39.

    Sharroush, S. M., Abdalla, Y. S., Dessouki, A. A. & El-Badawy, E. S. A. Subthreshold MOSFET transistor amplifier operation. In Proceedings of the 4th International Design Test Workshop (IDT) 1–6 (IEEE, 2009).

  40. 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. 41.

    Caporale, N. & Dan, Y. Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008).

    Article  Google Scholar 

  42. 42.

    Brivio, S. et al. Extended memory lifetime in spiking neural networks employing memristive synapses with nonlinear conductance dynamics. Nanotechnology 30, 015102 (2018).

    Article  Google Scholar 

  43. 43.

    Hu, S. G. et al. Associative memory realized by a reconfigurable memristive Hopfield neural network. Nat. Commun. 6, 7522 (2015).

    Article  Google Scholar 

  44. 44.

    Verleysen, M., Sirletti, B., Vandemeulebroecke, A. & Jespers, P. G. A. A high-storage capacity content-addressable memory and its learning algorithm. IEEE Trans. Circ. Syst. 36, 762–766 (1989).

    Article  Google Scholar 

  45. 45.

    Tank, D. & Hopfield, J. J. Simple ‘neural’ optimization networks: an A/D converter, signal decision circuit and a linear programming circuit. IEEE Trans. Circ. Syst. 33, 533–541 (1986).

    Article  Google Scholar 

  46. 46.

    Hopfield, J. J Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl Acad. Sci. USA 81, 3088–3092 (1984).

    Article  Google Scholar 

  47. 47.

    Sandrini, J. et al. Effect of metal buffer layer and thermal annealing on HfOx-based ReRAMs. In Proceedings of the IEEE International Conference on the Science of Electrical Engineering (ICSEE) 1–5 (IEEE, 2016).

  48. 48.

    Ko, P. K., Hu, C. & Tam, S. Lucky-electron model of channel hot-electron injection in MOSFET’s. IEEE Trans. Electron Dev. 31, 1116–1125 (1984).

    Article  Google Scholar 

  49. 49.

    Chan, T. Y., Chen, J., Ko, P. K. & Hu, C. The impact of gate-induced drain leakage current on MOSFET scaling. In Proceedings of the International Electron Devices Meeting (IEDM) 718–721 (IEEE, 1987).

  50. 50.

    Zidan, M. A., Fahmy, H. A. H., Hussain, M. M. & Salama, K. N. Memristor-based memory: the sneak paths problem and solutions. Microelectron. J. 44, 176–183 (2013).

    Article  Google Scholar 

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Acknowledgements

This research was partially supported by the Israeli Planning and Budgeting Committee Fellowship, by the Israel Ministry of Economics KAMIN project no. 57681, by the Andrew and Erna Finci Viterbi Graduate Fellowship and by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme under agreement no. 757259.

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Contributions

L.D., Y.R., R.D. and S.K. designed the study. L.D., E.H. and E.P. performed experiments and collected data. E.P. and Y.R. invented the Y-flash memristive structure and L.D., R.D. and S.K. invented the subthreshold small-signal memristive operation mode. L.D. developed models and executed simulations. All authors analysed the data, discussed the results and wrote the manuscript.

Corresponding author

Correspondence to Shahar Kvatinsky.

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Supplementary Information

Supplementary Sections 1–6 and Tables 1–10.

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Danial, L., Pikhay, E., Herbelin, E. et al. Two-terminal floating-gate transistors with a low-power memristive operation mode for analogue neuromorphic computing. Nat Electron 2, 596–605 (2019). https://doi.org/10.1038/s41928-019-0331-1

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