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Intrinsic plasticity of silicon nanowire neurotransistors for dynamic memory and learning functions

A Publisher Correction to this article was published on 16 June 2020

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Abstract

Neuromorphic architectures merge learning and memory functions within a single unit cell and in a neuron-like fashion. Research in the field has been mainly focused on the plasticity of artificial synapses. However, the intrinsic plasticity of the neuronal membrane is also important in the implementation of neuromorphic information processing. Here we report a neurotransistor made from a silicon nanowire transistor coated by an ion-doped sol–gel silicate film that can emulate the intrinsic plasticity of the neuronal membrane. The neurotransistors are manufactured using a conventional complementary metal–oxide–semiconductor process on an 8-inch (200 mm) silicon-on-insulator wafer. Mobile ions allow the film to act as a pseudo-gate that generates memory and allows the neurotransistor to display plasticity. We show that multiple pulsed input signals of the neurotransistor are non-linearly processed by sigmoidal transformation into the output current, which resembles the functioning of a neuronal membrane. The output response is governed by the input signal history, which is stored as ionic states within the silicate film, and thereby provides the neurotransistor with learning capabilities.

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Fig. 1: Structure and electrical characteristics of Si NW neurotransistors.
Fig. 2: Intrinsic plasticity of neurotransistors.
Fig. 3: Non-linear information processing of the neurotransistor with multiple inputs and outputs.
Fig. 4: History-dependent dynamic memory and learning within a single neurotransistor and its network.

Data availability

The source data for Figs. 14 are available within the paper and its Supplementary Information.

Code availability

The MatLab code that supports the mathematical model in this article is available at https://github.com/eunhye8747/MatLab-Code-Neurotransistor.

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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).

    Google Scholar 

  2. Cannistraci, C. V. Modelling self-organization in complex networks via a brain-inspired network automata theory improves link reliability in protein interactomes. Sci. Rep. 8, 15760 (2018).

    Google Scholar 

  3. Hasler, J. Special report: can we copy the brain? A road map for the artificial brain. IEEE Spectr. 54, 46–50 (2017).

    Google Scholar 

  4. Sengupta, B. & Stemmler, M. B. Power consumption during neuronal computation. Proc. IEEE 102, 738–750 (2014).

    Google Scholar 

  5. Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990).

    Google Scholar 

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

    Google Scholar 

  7. Traversa, F. L. & Di Ventra, M. Universal memcomputing machines. IEEE Trans. Neural Netw. Learn. Syst. 26, 2702–2715 (2015).

    MathSciNet  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  10. Wang, Z. Q. et al. Synaptic learning and memory functions achieved using oxygen ion migration/diffusion in an amorphous InGaZnO memristor. Adv. Funct. Mater. 22, 2759–2765 (2012).

    Google Scholar 

  11. Yu, S. et al. A low energy oxide-based electronic synaptic device for neuromorphic visual systems with tolerance to device variation. Adv. Mater. 25, 1774–1779 (2013).

    Google Scholar 

  12. Zhu, L. Q. et al. Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nat. Commun. 5, 3158 (2014).

    Google Scholar 

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

    Google Scholar 

  14. Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 1–13 (2017).

    Google Scholar 

  15. 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).

    Google Scholar 

  16. Sheridan, P. M. et al. Sparse coding with memristor networks. Nat. Nanotechnol. 12, 784–789 (2017).

    Google Scholar 

  17. Choi, S., Shin, J. H., Lee, J., Sheridan, P. & Lu, W. D. Experimental demonstration of feature extraction and dimensionality reduction using memristor networks. Nano Lett. 17, 3113–3118 (2017).

    Google Scholar 

  18. Nayak, A. K. et al. Magnetic antiskyrmions above room temperature in tetragonal Heusler materials. Nature 548, 561–566 (2017).

    Google Scholar 

  19. Fukami, S., Zhang, C., DuttaGupta, S., Kurenkov, A. & Ohno, H. Magnetization switching by spin–orbit torque in an antiferromagnet–ferromagnet bilayer system. Nat. Mater. 15, 535–541 (2016).

    Google Scholar 

  20. Grollier, J., Querlioz, D. & Stiles, M. D. Spintronic nanodevices for bioinspired computing. Proc. IEEE 104, 2024–2039 (2016).

    Google Scholar 

  21. Torrejon, J. et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature 547, 428–431 (2017).

    Google Scholar 

  22. Lai, Q. et al. Ionic/electronic hybrid materials integrated in a synaptic transistor with signal processing and learning functions. Adv. Mater. 22, 2448–2453 (2010).

    Google Scholar 

  23. Shi, J. et al. A correlated nickelate synaptic transistor. Nat. Commun. 4, 2676 (2013).

    Google Scholar 

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

    Google Scholar 

  25. Xu, W., Min, S.-Y., Hwang, H. & Lee, T.-W. Organic core–sheath nanowire artificial synapses with femtojoule energy consumption. Sci. Adv. 2, e1501326 (2016).

    Google Scholar 

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

    Google Scholar 

  27. Gkoupidenis, P., Schaefer, N., Garlan, B. & Malliaras, G. G. Neuromorphic functions in PEDOT:PSS organic electrochemical transistors. Adv. Mater. 27, 7176–7180 (2015).

    Google Scholar 

  28. Wang, Z. et al. Capacitive neural network with neuro-transistors. Nat. Commun. 9, 3208 (2018).

    Google Scholar 

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

    Google Scholar 

  30. Zhang, W. & Linden, D. J. The other side of the engram: experience-driven changes in neuronal intrinsic excitability. Nat. Rev. Neurosci. 4, 885–900 (2003).

    Google Scholar 

  31. Kemenes, I. et al. Role of delayed nonsynaptic neuronal plasticity in long-term associative memory. Curr. Biol. 16, 1269–1279 (2006).

    Google Scholar 

  32. Sánchez-Aguilera, A., Sánchez-Alonso, J. L., Vicente-Torres, M. A. & Colino, A. A novel short-term plasticity of intrinsic excitability in the hippocampal CA1 pyramidal cells. J. Physiol. 592, 2845–2864 (2014).

    Google Scholar 

  33. Mozzachiodi, R. & Byrne, J. H. More than synaptic plasticity: role of nonsynaptic plasticity in learning and memory. Trends Neurosci. 33, 17–26 (2010).

    Google Scholar 

  34. Kourrich, S., Calu, D. J. & Bonci, A. Intrinsic plasticity: an emerging player in addiction. Nat. Rev. Neurosci. 16, 173–184 (2015).

    Google Scholar 

  35. Hasler, J. & Marr, B. Finding a roadmap to achieve large neuromorphic hardware systems. Front. Neurosci. 7, 1–29 (2013).

    Google Scholar 

  36. He, Y. et al. Spatiotemporal information processing emulated by multiterminal neuro-transistor networks. Adv. Mater. 31, 1900903 (2019).

    Google Scholar 

  37. Tino, P., Benuskova, L. & Sperduti, A. in Springer Handbook of Computational Intelligence (eds Kacprzyk, J. & Pedrycz, W.) 455–471 (Springer, 2015).

  38. Ciriminna, R. et al. The sol–gel route to advanced silica-based materials and recent applications. Chem. Rev. 113, 6592–6620 (2013).

    Google Scholar 

  39. Baek, E. et al. Negative photoconductance in heavily doped Si nanowire field-effect transistors. Nano Lett. 17, 6727–6734 (2017).

    Google Scholar 

  40. Rim, T. et al. Electrical characteristics of doped silicon nanowire channel field-effect transistor biosensors. IEEE Sens. J. 17, 667–673 (2017).

    Google Scholar 

  41. Kim, K. et al. Silicon nanowire biosensors for detection of cardiac troponin I (cTnI) with high sensitivity. Biosens. Bioelectron. 77, 695–701 (2016).

    Google Scholar 

  42. Kim, D. M. Jeong, Y.-H. (eds) Nanowire Field Effect Transistors: Principles and Applications (Springer, 2014).

  43. Ibarlucea, B. et al. Nanowire sensors monitor bacterial growth kinetics and response to antibiotics. Lab Chip 17, 4283–4293 (2017).

    Google Scholar 

  44. Okhonin, S., Nagoga, M., Carman, E., Beffa, R. & Faraoni, E. New generation of Z-RAM. In Proc. 2007 IEEE International Electron Devices Meeting 925–928 (IEEE, 2007).

  45. Bawedin, M., Cristoloveanu, S. & Flandre, D. A capacitorless 1T-DRAM on SOI based on dynamic coupling and double-gate operation. IEEE Electron Device Lett. 29, 795–798 (2008).

    Google Scholar 

  46. Wan, J., Le Royer, C., Zaslavsky, A. & Cristoloveanu, S. Progress in Z 2 -FET 1T-DRAM: retention time, writing modes, selective array operation, and dual bit storage. Solid State Electron. 84, 147–154 (2013).

    Google Scholar 

  47. Cho, H. et al. Optimization of signal to noise ratio in silicon nanowire ISFET sensors. IEEE Sens. J. 17, 2792–2796 (2017).

    Google Scholar 

  48. Rim, T. et al. Improved electrical characteristics of honeycomb nanowire ISFETs. IEEE Electron Device Lett. 34, 1059–1061 (2013).

    Google Scholar 

  49. Rodriguez, O. R., Gill, W. N., Plawsky, J. L., Tsui, T. Y. & Grunow, S. Study of Cu diffusion in porous dielectrics using secondary-ion-mass spectrometry. J. Appl. Phys. 98, 123514 (2005).

    Google Scholar 

  50. Huguenard, J. R. & McCormick, D. A. Simulation of the currents involved in rhythmic oscillations in thalamic relay neurons. J. Neurophysiol. 68, 1373–1383 (1992).

    Google Scholar 

  51. Hodgkin, A. L. & Huxley, A. F. A quantitative description of membrane current and its application to conduction and excitation in nerves. J. Physiol. 117, 500–544 (1952).

    Google Scholar 

  52. Turrigiano, G. G. & Nelson, S. B. Homeostatic plasticity in the developing nervous system. Nat. Rev. Neurosci. 5, 97–107 (2004).

    Google Scholar 

  53. Watt, A. J. & Desai, N. S. Homeostatic plasticity and STDP: keeping a neuron’s cool in a fluctuating world. Front. Synaptic Neurosci. 2, 1–16 (2010).

    Google Scholar 

  54. Sehgala, M., Song, C., Ehlers, V. L. & Moyer, R. J. Jr Learning to learn—intrinsic plasticity as a metaplasticity mechanism for memory formation. Neurobiol. Learn. Mem. 105, 186–199 (2013).

    Google Scholar 

  55. Zucker, R. S. & Regehr, W. G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002).

    Google Scholar 

  56. Wickliffe, C. Abraham Metaplasticity: tuning synapses and networks for plasticity. Nat. Rev. Neurosci. 9, 387–399 (2008).

    Google Scholar 

Download references

Acknowledgements

This research was supported by the German Excellence Initiative via the Cluster of Excellence EXC1056 Center for Advancing Electronics Dresden (CfAED) and the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ICT Consilience Creative Program (IITP-R0346-16-1007) supervised by the IITP (Institute for Information & communications Technology Promotion). We acknowledge support from the Initiative and Networking Fund of the Helmholtz Association of German Research Centers through the International Helmholtz Research School for Nanoelectronic Networks (IHRS NANONET) (no. VH‐KO‐606) and German Research Foundation (DFG) via grants MA 5144/9-1, MA 5144/13-1 and MA 5144/14-1; BA4986/7−1, BA4986/8−1. Finally, we thank the INSA-DFG Bilateral Exchange Programme for financial support (IA/DFG/2018/138, 12 April 2018). The authors thank S. Oswald (IFW Dresden) for the X-ray photoemission spectroscopy analysis of the ion-doped hybrid silicate films and M. Park (NamLab, Dresden) for the insightful discussion about the ionic polarization in the film. We thank R. Nigmetzianov (TU Dresden) for the film analysis.

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Authors and Affiliations

Authors

Contributions

E.B. developed the idea of neurotransistors and their network for the supervised learning tasks, designed corresponding experiments and analysed the data. N.R.D. designed the LT-SPICE circuit simulations and mathematical models derived from the physics of the neurotransistor. C.V.C. figured out the sigmoidal growth of the output curve and its functionality in the single neural network, and discovered the correlation between the intrinsic plasticity of the device and the neuronal membrane. K.N. developed the hardware for network applications of neurotransistors. T.R. designed and supplied the Si NW FET devices. G.S.C.B. and D.M. provided support in the pulse measurements. H.C. and K.K. fabricated the transistors and revised the manuscript. C.-K.B. supported the research stay of E.B. for the electrical analysis of the device in POSTECH. R.T. and L.C. devised ideas to model the neurotransistors and to realize the single-layer neural network. L.B. supervised the research and contributed to the discussion of the overall methodology and results. G.C. contributed to the discussion and supporting network and infrastructure of the research. E.B., C.V.C., D.M., L.B. and G.C. co-wrote and modified the manuscript.

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Correspondence to Eunhye Baek, Larysa Baraban or Gianaurelio Cuniberti.

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Supplementary Information

Supplementary Figs. 1–13 and Tables 1 and 2.

Source data

Source Data Fig. 1

Excel tables to plot Fig.1.

Source Data Fig. 2

Excel tables to plot Fig. 2.

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Excel tables to plot Fig. 3.

Source Data Fig. 4

Excel tables to plot Fig. 4.

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Baek, E., Das, N.R., Cannistraci, C.V. et al. Intrinsic plasticity of silicon nanowire neurotransistors for dynamic memory and learning functions. Nat Electron 3, 398–408 (2020). https://doi.org/10.1038/s41928-020-0412-1

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