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Recently, integrated optics has become a functional platform for implementing machine learning algorithms and, in particular, neural networks. Photonic integrated circuits can straightforwardly perform vector-matrix multiplications with high efficiency and low power consumption by using weighting mechanism through linear optics. However, this cannot be said for the activation function, i.e., “threshold,” which requires either nonlinear optics or an electro-optic module with an appropriate dynamic range. Even though all-optical nonlinear optics is potentially faster, its current integration is challenging and is rather inefficient. Here, we demonstrate an electroabsorption modulator based on an indium tin oxide layer monolithically integrated into silicon photonic waveguides, whose dynamic range is used as a nonlinear activation function of a photonic neuron. The thresholding mechanism is based on a photodiode, which integrates the weighed products, and whose photovoltage drives the electroabsorption modulator. The synapse and neuron circuit is then constructed to execute a 200-node MNIST classification neural network used for benchmarking the nonlinear activation function and compared with an equivalent electronic module.
Sprache
Englisch
Identifikatoren
ISSN: 2166-532X
eISSN: 2166-532X
DOI: 10.1063/1.5109039
Titel-ID: cdi_scitation_primary_10_1063_1_5109039
Format
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