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'.
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- Ring resonator on the output: to match the polarity of the phase-change material. Is this for reset? Storing light until trigger?
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- 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
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