Neural network enabled time stretch spectral regression.

Abstract

Spectral interferometry is utilized in a wide range of biomedical and scientific applications and metrology. Retrieving the magnitude and phase of the complex electric field from the interferogram is central to all its applications. We report a spectral interferometry system that utilizes a neural network to infer the magnitude and phase of femtosecond interferograms directly from the measured single-shot interference patterns and compare its performance with the widely used Hilbert transform. Our approach does not require apriori knowledge of the shear frequency, and achieves higher accuracy under our experimental conditions. To train the network, we introduce an experimental technique that generates a large number of femtosecond interferograms with known (labeled) phase and magnitude profiles. While the profiles for these pulses are digitally generated, they obey causality by satisfying the Kramer-Kronig relation. This technique is resilient against nonlinear optical distortions, quantization noise, and the sampling rate limit of the backend digitizer - valuable properties that relax instrument complexity and cost.