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Phase-change memtransistive synapses for mixed-plasticity neural computations

Nature Nanotechnology volume 17, pages 507–513 (2022)Cite this article

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Abstract

In the mammalian nervous system, various synaptic plasticity rules act, either individually or synergistically, over wide-ranging timescales to enable learning and memory formation. Hence, in neuromorphic computing platforms, there is a significant need for artificial synapses that can faithfully express such multi-timescale plasticity mechanisms. Although some plasticity rules have been emulated with elaborate complementary metal oxide semiconductor and memristive circuitry, device-level hardware realizations of long-term and short-term plasticity with tunable dynamics are lacking. Here we introduce a phase-change memtransistive synapse that leverages both the non-volatility of the phase configurations and the volatility of field-effect modulation for implementing tunable plasticities. We show that these mixed-plasticity synapses can enable plasticity rules such as short-term spike-timing-dependent plasticity that helps with the modelling of dynamic environments. Further, we demonstrate the efficacy of the memtransistive synapses in realizing accelerators for Hopfield neural networks for solving combinatorial optimization problems.

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Fig. 1: Synaptic efficacy and phase-change memtransistors.
Fig. 2: Dynamics of memtransistive synapses.
Fig. 3: Recognizing sequential data with short-term spike time-dependent plasticity.
Fig. 4: Tunable and weight-dependent short-term dynamics.
Fig. 5: Combinatorial optimization using global plasticity.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We acknowledge funding for this work from the European Union’s Horizon 2020 Research and Innovation Programme (Fun-COMP project no. 780848) and from the European Research Council through the European Union’s Horizon 2020 Research and Innovation Programme under grant no. 682675. We also acknowledge technical input from A. Rahimi (Research Scientist, IBM Research-Zurich). We thank the cleanroom operations team of the Binnig and Rohrer Nanotechnology Center for their support.

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Authors and Affiliations

  1. IBM Research – Europe, Rüschlikon, Switzerland

    Syed Ghazi Sarwat, Benedikt Kersting, Timoleon Moraitis, Vara Prasad Jonnalagadda & Abu Sebastian

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  1. Syed Ghazi Sarwat
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  2. Benedikt Kersting
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  3. Timoleon Moraitis
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  4. Vara Prasad Jonnalagadda
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  5. Abu Sebastian
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Contributions

S.G.S and A.S. conceptualized the idea. S.G.S designed algorithms, performed experiments and analysed data with assistance from B.K and T.M. V.P.J., S.G.S. and B.K. fabricated the devices. S.G.S wrote the manuscript with input from all the authors.

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Correspondence to Syed Ghazi Sarwat or Abu Sebastian.

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Nature Nanotechnology thanks Paschalis Gkoupidenis, Xiangshui Miao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary discussion and Figs. 1–9.

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Sarwat, S.G., Kersting, B., Moraitis, T. et al. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nat. Nanotechnol. 17, 507–513 (2022). https://doi.org/10.1038/s41565-022-01095-3

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