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Fully memristive neural networks for pattern classification with unsupervised learning

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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Fig. 1: Diffusive memristor artificial neuron.
Fig. 2: Controlled firing of a diffusive memristor artificial neuron.
Fig. 3: Experimental demonstration of unsupervised synaptic weight update using a 2 × 2 drift memristor array interfaced with two diffusive memristor artificial neurons, illustrating circuits with large and small capacitance.
Fig. 4: Fully integrated memristive neural network for pattern classification.
Fig. 5: Unsupervised training of a fully connected network based on the integrated all-memristive neural network.

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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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Correspondence to R. Stanley Williams or Qiangfei Xia or J. Joshua Yang.

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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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