Letter | Published:

Training and operation of an integrated neuromorphic network based on metal-oxide memristors

Nature volume 521, pages 6164 (07 May 2015) | Download Citation

Abstract

Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex1, with its approximately 1014 synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks2 based on circuits3,4 combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one3 or several4 crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits5,6,7,8,9,10,11,12, including first demonstrations5,6,12 of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks13,14,15,16,17,18. Very recently, such experiments have been extended19 to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors11,20,21, whose nonlinear current–voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm22 to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.

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Acknowledgements

We acknowledge useful discussions with F. Alibart, I. Kataeva, W. Lu, L. Sengupta, S. Stemmer, and E. Zamanidoost. This work was supported by the AFOSR under the MURI grant FA9550-12-1-0038, by DARPA under contract number HR0011-13-C-0051UPSIDE via BAE Systems, and by the DENSO Corporation, Japan.

Author information

Author notes

    • M. Prezioso
    • , F. Merrikh-Bayat
    •  & B. D. Hoskins

    These authors contributed equally to this work.

Affiliations

  1. Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, California 93106, USA

    • M. Prezioso
    • , F. Merrikh-Bayat
    • , B. D. Hoskins
    • , G. C. Adam
    •  & D. B. Strukov
  2. Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, USA

    • K. K. Likharev

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Contributions

M.P., F.M.-B., B.D.H., K.K.L., and D.B.S. designed the research. M.P., B.D.H., and G.C.A. performed fabrication and device testing. M.P. and F.M.-B. performed pattern classifier experiments. All authors discussed and interpreted results. M.P., K.K.L., and D.B.S. wrote the manuscript. K.K.L. and D.B.S. advised on all parts of the project.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to M. Prezioso or D. B. Strukov.

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    Supplementary Information

    This file contains Supplementary Text and Data 1-7, Supplementary Figures 1-12 and additional references.

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DOI

https://doi.org/10.1038/nature14441

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