Advanced Data Analytics and Supervised Machine Learning in Optics Engineering Analysis

Abstract

Advance data analytics and machine learning have affected almost every industry and area of scientific research, including engineering. Although limited literature of Machine Learning in optics engineering are found, Machine learning adoption has been valuable and garners a lot of interest in this field [1][2], and the rate of research in this area is growing rapidly [3]. In fiber optic transmission system, an optical transceiver is a core element, responsible for converting electrical signal to light pulses and vice versa. It comprises of housing, optoelectronic devices and PCBA, it has to undergo various characterization and tests at different stages of the manufacturing processes. Optical transceiver characterization is a very complex process with many sub-processes and parameters within those sub-processes which can lead to difficulties using traditional analytics approach. Usually, a tuning process only utilizes key parametric at the point of characterization, it may not be optimized taking considerations of other external factors e.g. product variants, components, testers, software used etc. Machine Learning shines when there are a lot of input parameters to be optimized [1]. This paper describes the application of machine learning techniques in the transmitter characterization algorithm of a high speed optical transceiver module to enhance the tuning algorithm and also improving throughput.