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A feedforward optical neural network for image recognition is demonstrated through free-space optics in which the nonlinear activation function is provided by a resonant semiconductor saturable absorption mirror (RSAM). Although free-space optics with liquid crystal based spatial light modulator has the potential to allow parallel processing for improved performance, using a fiber-optic electrooptic modulator for parallel to serial conversion greatly simplifies the experimental setup. We show that this nonlinear activation function introduced by RSAM can provide a notable improvement of 8.1% to classification accuracy in comparison to a purely linear network when tested with the MNIST data set for image classification. The impact of noise is also investigated in system implementation.
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ITO-based electro-absorption modulator for photonic neural activation function
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.
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- Award ID(s):
- 1740262
- PAR ID:
- 10594985
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- APL Materials
- Volume:
- 7
- Issue:
- 8
- ISSN:
- 2166-532X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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