Optical Wavelength Meter with Machine Learning Enhanced Precision

TL;DR

This paper presents a machine learning-enhanced optical wavelength measurement method that compensates for interferometer impairments, significantly improving precision over traditional techniques through simulation and experimental validation.

Contribution

It introduces a novel inverse mapping approach using machine learning to correct impairments in photonic interferometers for more accurate wavelength measurements.

Findings

Over an order of magnitude improvement in measurement precision

Robustness to noise and impairments demonstrated in simulations

Experimental validation on a Si3N4 platform confirms effectiveness

Abstract

Diverse applications in photonics and microwave engineering require a means of measurement of the instantaneous frequency of a signal. A photonic implementation typically applies an interferometer equipped with three or more output ports to measure the frequency dependent phase shift provided by an optical delay line. The components constituting the interferometer are prone to impairments which results in erroneous measurements. It is shown that the information to be retrieved is encoded by a three-component vector that lies on a circular cone within a three-dimensional Cartesian object space. The measured data belongs to the image of the object space under a linear map that describes the action of the interferometer. Assisted by a learning algorithm, an inverse map from the image space into the object space is constructed. The inverse map compensates for a variety of impairments while being robust to noise. Simulation results demonstrate that, to the extent the interferometer model captures all significant impairments, a precision limited only by the level of random noise is attainable. A wavelength meter architecture is fabricated on Si3N4 photonic integration platform to prove the method experimentally. Applied to the measured data, greater than an order of magnitude improvement in precision is achieved by the proposed method compared to the conventional method.