Inverse Design of FBG-Based Optical Filters Using Deep Learning: A Hybrid CNN-MLP Approach

Optical filters have always been a critical challenge for advancing communication systems. Despite significant progress in optical filters, the process of designing, fabricating, and characterizing filters has predominantly followed an iterative approach. In this approach, the designer makes an educated guess regarding the filter's structure and subsequently solves Maxwell's equations to determine its performance. Conversely, the inverse problem, which involves obtaining a filter geometry to achieve a desired electromagnetic response, continues to pose challenges and demands considerable time and effort, particularly within the constraints of particular assumptions. More recently, the inverse design problem in optics and photonics has been tackled by deep learning, but the focus was not on the optical filters. This study aims to fill this research gap. This article develops a hybrid deep learning methodology to address the inverse design challenges of optical filters based on fiber Bragg gratings (FBGs), eliminating the dependency on empirical or trial-and-error methods. We utilized a convolutional neural network (CNN) to derive an apodization profile that facilitates the design of a narrow, dual-channel filter with identical channels, each featuring a 0.75 nm bandwidth, ideally suited for optical communication systems. Our model, which also employs a multilayer perceptron in addition to CNN, demonstrates artificial intelligence's efficacy in designing FBG-based filters. The model achieves low predictive losses on the order of 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> for both the forward and inverse models.