Design of an equal-power dual-peak Brillouin gain spectrum based on M-shaped optical fibers transitioning from ring to circular core for temperature and curvature sensing.

Here we design a simple M-shaped optical fiber to generate equal-power dual Brillouin gain peaks, and numerically simulate bending loss-resistant temperature and curvature sensing. By investigating the M-shaped fibers transitioning from ring-core to circular-core, we examine the Brillouin gain spectrum evolution from a single peak to dual peaks and back to a single peak. During this fiber transition and spectral evolution, we find that the calculated Brillouin frequency shift (BFS) and Brillouin gain exhibit unique developments based on acoustic-optic coupling theory, providing a methodology for designing and optimizing a desirable Brillouin gain spectrum in M-shaped optical fibers.

Artificial neural network assisted the design of subwavelength-grating waveguides for nanoparticles optical trapping.

In this work, we propose artificial neural networks (ANNs) to predict the optical forces on particles with a radius of 50 nm and inverse-design the subwavelength-grating (SWG) waveguides structure for trapping. The SWG waveguides are applied to particle trapping due to their superior bulk sensitivity and surface sensitivity, as well as longer working distance than conventional nanophotonic waveguides. To reduce the time consumption of the design, we train ANNs to predict the trapping forces and to inverse-design the geometric structure of SWG waveguides, and the low mean square errors (MSE) of the networks achieve 2.