A new heuristic method for optimizing Y-branches using genetic algorithm with optimal dataset generated with particle swarm optimization

The Genetic Algorithm (GA) is one of the most popular heuristic methods due to its natural and fast implementation. However, at the same time, it has the disadvantage of poor optimization. To improve performance, it's necessary avoid stuck in local maximums throught choosing proper methods and parameters that vary for each application. In photonic devices, although the GA has been recently used to optimize passive silicon Y-branches, its performance is still trailing behind other optimization algorithms based on swarms, for instance. In this work, we present a new three-part heuristic method for optimizing Y-branches. We used the Finite-difference Time-domain (FDTD) method and the Particle Swarm Optimization (PSO) to generate an optimal data set as initial population for the GA. Considering an adequate population model, we demonstrate improvement in the performance for the design of a Y-branch through the GA. Next, we used a variation of a gradient-based search method to fine-tune the final parameters to find the absolute maximum. As a result, we produced new non-intuitive Y-branch devices with on-chip areas smaller than 2µm2 and excess loss down to 0.05 dB @1550 nm for the TE mode. A complete study of fabrication feasibility and uv-lithography typical fabrication errors and its effects on the bandwidth will be shown at the time of the conference. Our method will be compared against other widely-used heuristic methods in photonic device design in terms of number of iterations.