Discovering extremely low confinement-loss anti-resonant fibers via swarm intelligence.

In this work, we obtain extremely low confinement-loss (CL) anti-resonant fibers (ARFs) via swarm intelligence, specifically the particle swarm optimization (PSO) algorithm. We construct a complex search space of ARFs with two layers of cladding and nested tubes. There are three and four structures of cladding tubes in the first and second layer, respectively. The ARFs are optimized by using the PSO algorithm in terms of both the structures and the parameters. The optimal structure is obtained from a total of 415900 ARFs structures, with the lowest CL being 2.839×10 dB/m at a wavelength of 1.55 µm. We observe that the number of ARF structures with CL less than 1×10 dB/m in our search space is 370. These structures mainly comprise four designs of ARFs. The results show that the optimal ARF structures realized by the PSO algorithm are different from the ARFs reported in the previous literature. This means that the swarm intelligence accelerates the design and invention of ARFs and also provides new insights regarding the ARF structures. This work provides a fast and effective approach to design ARFs with special requirements. In addition to providing high-performance ARF structures, this work transforms the ARF designs from experience-driven to data-driven.