Fiber directional position sensor based on multimode interference imaging and machine learning.

Abstract

A fiber directional position sensor based on multimode interference and image processing by machine learning is presented. Upon single-mode injection, light in multimode fiber generates a multi-ring-shaped interference pattern at the end facet, which is susceptible to the amplitude and direction of the fiber distortions. The fiber is mounted on an automatic translation stage, with repeating movement in four directions. The images are captured from an infrared camera and fed to a machine-learning program to train, validate, and test the fiber conditions. As a result, accuracy over 97% is achieved in recognizing fiber positions in these four directions, each with 10 classes, totaling an 8 mm span. The number of images taken for each class is merely 320. Detailed investigation reveals that the system can achieve over 60% accuracy in recognizing positions on a 5 µm resolution with a larger dataset, approaching the limit of the chosen translation stage.