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ref: -2012 tags: phase change materials neuromorphic computing synapses STDP date: 06-13-2019 21:19 gmt revision:3 [2] [1] [0] [head]

Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-Inspired Computing

  • Here, we report a new nanoscale electronic synapse based on technologically mature phase change materials employed in optical data storage and nonvolatile memory applications.
  • We utilize continuous resistance transitions in phase change materials to mimic the analog nature of biological synapses, enabling the implementation of a synaptic learning rule.
  • We demonstrate different forms of spike-timing-dependent plasticity using the same nanoscale synapse with picojoule level energy consumption.
  • Again uses GST germanium-antimony-tellurium alloy.
  • 50pJ to reset (depress) the synapse, 0.675pJ to potentiate.
    • Reducing the size will linearly decrease this current.
  • Synapse resistance changes from 200k to 2M approx.

See also: Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

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ref: -2019 tags: optical neural networks spiking phase change material learning date: 06-01-2019 19:00 gmt revision:4 [3] [2] [1] [0] [head]

All-optical spiking neurosynaptic networks with self-learning capabilities

  • J. Feldmann, N. Youngblood, C. D. Wright, H. Bhaskaran & W. H. P. Pernice
  • Idea: use phase-change material to either block or pass the light in waveguides.
    • In this case, they used GST -- germanium-antimony-tellurium. This material is less reflective in the amorphous phase, which can be reached by heating to ~150C and rapidly quenching. It is more reflective in the crystalline phase, which occurs on annealing.
  • This is used for both plastic synapses (phase change driven by the intensity of the light) and the nonlinear output of optical neurons (via a ring resonator).
  • Uses optical resonators with very high Q factors to couple different wavelengths of light into the 'dendrite'.
  • Ring resonator on the output: to match the polarity of the phase-change material. Is this for reset? Storing light until trigger?
  • Were able to get correlative-like or hebbian learning (which I suppose is not dissimilar from really slow photographic film, just re-branded, and most importantly with nonlinear feedback.)
  • Issue: every weight needs a different source wavelength! Hence they have not demonstrated a multi-layer network.
  • Previous paper: All-optical nonlinear activation function for photonic neural networks
    • Only 3db and 7db extinction ratios for induced transparency and inverse saturation