{"title":"Chirped apodized fiber Bragg gratings inverse design via deep learning","authors":"","doi":"10.1016/j.optlastec.2024.111766","DOIUrl":null,"url":null,"abstract":"<div><p>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<sup>-2</sup> and 1.6 × 10<sup>-2</sup>, respectively. The FBG configurations derived via DL are ideally suited for optical communications, heralding significant advancements in all-optical signal processing.</p></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optics and Laser Technology","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0030399224012246","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Optics & Laser Technology aims to provide a vehicle for the publication of a broad range of high quality research and review papers in those fields of scientific and engineering research appertaining to the development and application of the technology of optics and lasers. Papers describing original work in these areas are submitted to rigorous refereeing prior to acceptance for publication.
The scope of Optics & Laser Technology encompasses, but is not restricted to, the following areas:
•development in all types of lasers
•developments in optoelectronic devices and photonics
•developments in new photonics and optical concepts
•developments in conventional optics, optical instruments and components
•techniques of optical metrology, including interferometry and optical fibre sensors
•LIDAR and other non-contact optical measurement techniques, including optical methods in heat and fluid flow
•applications of lasers to materials processing, optical NDT display (including holography) and optical communication
•research and development in the field of laser safety including studies of hazards resulting from the applications of lasers (laser safety, hazards of laser fume)
•developments in optical computing and optical information processing
•developments in new optical materials
•developments in new optical characterization methods and techniques
•developments in quantum optics
•developments in light assisted micro and nanofabrication methods and techniques
•developments in nanophotonics and biophotonics
•developments in imaging processing and systems