Chirped apodized fiber Bragg gratings inverse design via deep learning

Overcoming inverse design problems in fiber Bragg gratings (FBGs) can be challenging due to the significant nonlinearity of the problem and the intricate relationship between structural properties and optical characteristics. Here, we present a novel artificial intelligence-based approach that effectively addresses these challenges. We introduce a methodology centered on applying deep learning (DL) to estimate the reflective spectrum of FBGs. The results highlight DL’s exceptional capability in designing chirped apodized FBGs, with our model demonstrating significantly enhanced computational efficiency relative to traditional numerical simulations. Notably, our DL-based approach exhibits the remarkable ability to tackle the inverse design challenges of FBGs, thereby eliminating the reliance on trial-and-error or empirical methodologies. The predictive losses for both the forward and inverse models are impressively minimal, with low loss values of 2.2 × 10^-2 and 1.6 × 10^-2, respectively. The FBG configurations derived via DL are ideally suited for optical communications, heralding significant advancements in all-optical signal processing.