Abstract
Neuromorphic computers comprised of artificial neurons and synapses could provide a more efficient approach to implementing neural network algorithms than traditional hardware. Recently, artificial neurons based on memristors have been developed, but with limited bio-realistic dynamics and no direct interaction with the artificial synapses in an integrated network. Here we show that a diffusive memristor based on silver nanoparticles in a dielectric film can be used to create an artificial neuron with stochastic leaky integrate-and-fire dynamics and tunable integration time, which is determined by silver migration alone or its interaction with circuit capacitance. We integrate these neurons with nonvolatile memristive synapses to build fully memristive artificial neural networks. With these integrated networks, we experimentally demonstrate unsupervised synaptic weight updating and pattern classification.
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References
Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354 (2017).
Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).
Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Lett. 10, 1297–1301 (2010).
Yu, S., Wu, Y., Jeyasingh, R., Kuzum, D. & Wong, H. S. P. An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation. IEEE Trans. Elect. Dev. 58, 2729–2737 (2011).
Ohno, T. et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (2011).
Pershin, Y. V. & Di Ventra, M. Neuromorphic, Digital, and Quantum Computation With Memory Circuit Elements. Proc. IEEE 100, 2071–2080 (2012).
Lim, H., Kim, I., Kim, J. S., Hwang, C. S. & Jeong, D. S. Short-term memory of TiO2-based electrochemical capacitors: empirical analysis with adoption of a sliding threshold. Nanotechnology 24, 384005 (2013).
Sheridan, P., Ma, W. & Lu, W. in Circuits and Systems (ISCAS), 2014 IEEE International Symposium on. 1078–1081 (IEEE).
La Barbera, S., Vuillaume, D. & Alibart, F. Filamentary Switching: Synaptic Plasticity through Device Volatility. ACS Nano 9, 941–949 (2015).
Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).
Hu, S. G. et al. Associative memory realized by a reconfigurable memristive Hopfield neural network. Nat. Commun. 6, 7522 (2015).
Serb, A. et al. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nat. Commun. 7, 12611 (2016).
Park, J. et al. TiOx-based RRAM synapse with 64-levels of conductance and symmetric conductance change by adopting a hybrid pulse scheme for neuromorphic computing. IEEE Elect. Dev. Lett. 37, 1559–1562 (2016).
Shulaker, M. M. et al. Three-dimensional integration of nanotechnologies for computing and data storage on a single chip. Nature 547, 74–78 (2017).
Suri, M. et al. in Electron Devices Meeting (IEDM), 2011 IEEE International. 4.4.1-4.4. 4 (IEEE).
Eryilmaz, S. B. et al. Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front. Neurosci. 8, 205 (2014).
Burr, G. W. et al. Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element. IEEE Trans. Elect. Dev. 62, 3498–3507 (2015).
Ambrogio, S. et al. Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses. Front. Neurosci. 10, 56 (2016).
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).
Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 73 (2011).
Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).
Sourikopoulos, I. et al. A 4-fJ/Spike Artificial Neuron in 65 nm CMOS Technology. Front. Neurosci. 11, 123 (2017).
Pickett, M. D., Medeiros-Ribeiro, G. & Williams, R. S. A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114–117 (2013).
Lim, H. et al. Reliability of neuronal information conveyed by unreliable neuristor-based leaky integrate-and-fire neurons: a model study. Sci. Rep. 5, 9776 (2015).
Stoliar, P. et al. A Leaky-Integrate-and-Fire Neuron Analog Realized with a Mott Insulator. Adv. Funct. Mater., 1604740, (2017).
Tuma, T., Pantazi, A., Le Gallo, M., Sebastian, A. & Eleftheriou, E. Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693–699 (2016).
Al-Shedivat, M., Naous, R., Cauwenberghs, G. & Salama, K. N. Memristors empower spiking neurons with stochasticity. IEEE Trans. Emerg. Sel. Top. Circuits Syst. 5, 242–253 (2015).
Mehonic, A. & Kenyon, A. J. Emulating the Electrical Activity of the Neuron Using a Silicon Oxide RRAM Cell. Front. Neurosci. 10, 57 (2016).
Gupta, I. et al. Real-time encoding and compression of neuronal spikes by metal-oxide memristors. Nat. Commun. 7, 12805 (2016).
Lim, H. et al. Relaxation oscillator-realized artificial electronic neurons, their responses, and noise. Nanoscale 8, 9629–9640 (2016).
Yang, Y. et al. Observation of conducting filament growth in nanoscale resistive memories. Nat. Commun. 3, 732 (2012).
Liu, Q. et al. Real-time observation on dynamic growth/dissolution of conductive filaments in oxide-electrolyte-based ReRAM. Adv. Mater. 24, 1844–1849 (2012).
Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2016).
Magee, J. C. Dendritic integration of excitatory synaptic input. Nat. Rev. Neurosci. 1, 181–190 (2000).
Gerstner, W. & Kistler, W. M. Spiking Neuron Models: Single Neurons, Populations, Plasticity. (Cambridge University Press, 2002).
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
Tsuruoka, T. et al. Effects of Moisture on the Switching Characteristics of Oxide-Based, Gapless-Type Atomic Switches. Adv. Funct. Mater. 22, 70–77 (2012).
Valov, I. et al. Atomically controlled electrochemical nucleation at superionic solid electrolyte surfaces. Nat. Mater. 11, 530–535 (2012).
Valov, I. et al. Nanobatteries in redox-based resistive switches require extension of memristor theory. Nat. Commun. 4, 1771 (2013).
Messerschmitt, F., Kubicek, M. & Rupp, J. L. M. How Does Moisture Affect the Physical Property of Memristance for Anionic-Electronic Resistive Switching Memories? Adv. Funct. Mater. 25, 5117–5125 (2015).
Valov, I. & Lu, W. D. Nanoscale electrochemistry using dielectric thin films as solid electrolytes. Nanoscale 8, 13828–13837 (2016).
Wong, H.-S. P. et al. Phase change memory. Proc. IEEE 98, 2201–2227 (2010).
Jeyasingh, R., Liang, J., Caldwell, M. A., Kuzum, D. & Wong, H.-S. P. in Custom Integrated Circuits Conference (CICC), 2012 IEEE. 1-7 (IEEE).
Mainen, Z. F. & Sejnowski, T. J. Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382, 363–366 (1996).
Roweis, S. T. & Saul, L. K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000).
Kim, S. et al. Experimental Demonstration of a Second-Order Memristor and Its Ability to Biorealistically Implement Synaptic Plasticity. Nano Lett. 15, 2203–2211 (2015).
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).
Tuma, T., Le Gallo, M., Sebastian, A. & Eleftheriou, E. Detecting Correlations Using Phase-Change Neurons and Synapses. IEEE Elect. Dev. Lett. 37, 1238–1241 (2016).
Pantazi, A., Wozniak, S., Tuma, T. & Eleftheriou, E. All-memristive neuromorphic computing with level-tuned neurons. Nanotechnology 27, 355205 (2016).
Sebastian, A. et al. Temporal correlation detection using computational phase-change memory. Nat. Commun. 8, 1115 (2017).
Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron.. https://doi.org/10.1038/s41928-41017-40002-z (2017).
Midya, R. et al. Anatomy of Ag/Hafnia-Based Selectors with 1010 Nonlinearity. Adv. Mater. 29, 1604457 (2017).
Jiang, H. et al. A novel true random number generator based on a stochastic diffusive memristor. Nat. Commun. 8, 882 (2017).
Acknowledgements
This work was supported in part by the U.S. Air Force Research Laboratory (AFRL) (Grant No. FA8750-15-2-0044), the Defense Advanced Research Projects Agency (DARPA) (Contract No. D17PC00304), the Intelligence Advanced Research Projects Activity (IARPA) (contract 2014-14080800008), and the National Science Foundation (NSF) (ECCS-1253073). H.W. was supported by Beijing Advanced Innovation Center for Future Chip (ICFC) and NSFC (61674089, 61674092). The authors would like to thank Ning Ge from HP Inc. and Mark McLean from Laboratory for Physical Sciences at Research Park for vaulable discussions. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of AFRL. Part of the device fabrication was conducted in the clean room of Center for Hierarchical Manufacturing (CHM), an NSF Nanoscale Science and Engineering Center (NSEC) located at the University of Massachusetts Amherst. The authors thank Mark McLean for useful discussions on computing.
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J.J.Y. conceived the concept. J.J.Y., Q.X., Z.W. and S.J. designed the experiments. Z.W., P.Y., and C.L. fabricated the devices. Z.W., S.J., W.S., Y.L, R.M., and M.R. performed electrical measurements. S.E.S. performed the simulation. S.A., Y.Z., H.J., P.L., J.H.Y., N.K.U., J.Z., M.H., J.P.S., M.B, Q.W., H.W., and R.S.W. helped with experiments and data analysis. J.J.Y., Q.X., Z.W., S.J., and R.S.W. wrote the paper. All authors discussed the results and implications and commented on the manuscript at all stages.
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Wang, Z., Joshi, S., Savel’ev, S. et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat Electron 1, 137–145 (2018). https://doi.org/10.1038/s41928-018-0023-2
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DOI: https://doi.org/10.1038/s41928-018-0023-2
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