Convolutional neural network classification of beam profiles from silicon photonics gratings

Abstract

Convolutional neural network (CNN) models consist of CNN block(s), and dense neural network (DNN) block(s) are used to perform image classification on beam profiles in light beams coupled out from silicon photonics (SiPh) mixed-pitch gratings. The beam profiles are first simulated and segregated into three categories based on their corresponding height above the SiPh gratings. With one CNN block, one DNN block, and 128 nodes in the DNN block, classification accuracy of 98.68% is achieved when classifying 454 beam profile images to their corresponding categories. Expanding the number of CNN blocks, DNN blocks, and nodes, 64 CNN models are constructed, trained, and evaluated. Out of the 64 CNN models, 52 of them achieved classification accuracy of >95%.