KEYWORDS: Analog electronics, Silicon photonics, Nanophotonics, Calibration, Phase shifts, Matrices, Machine learning, Signal to noise ratio, Databases, Data modeling
We show our recent progress on a Clements-type16x16 on-chip matrix processor based on silicon photonics and a new type of electro-optic digital-to-analog converters (EO DACs) with a higher signal-to-noise ratio. For the former, we developed a machine-learning-based calibration technique that involves theoretical modeling with circuit parameters (loss, phase error, splitting ratio, and crosstalks), which is adequate to obtain better fidelity for large-scale imperfect interferometers. After the calibration, we demonstrated a 16x16 identity matrix and several permutation matrices with a high signal-to-noise ratio and a well-known MNIST database classification task. For the latter, we developed low-loss and wavelength insensitive EO DACs consisting of 1:1 Y splitters and phase modulators that are useful for DAC-less input units for photonic accelerators.
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