Research on dual-objective directional coupler based on a two-step inverse design method

Yuanji Zhu; Qi Liu; Liuge Du; Yi Zou

doi:10.1117/12.3048026

13 December 2024 Research on dual-objective directional coupler based on a two-step inverse design method

Yuanji Zhu, Qi Liu, Liuge Du, Yi Zou

Author Affiliations +

Proceedings Volume 13499, AOPC 2024: Optical Devices and Integration; 134990R (2024) https://doi.org/10.1117/12.3048026
Event: Applied Optics and Photonics China 2024 (AOPC2024), 2024, Beijing, China

Abstract

Linear Weighting Method(LWM) and Multi-Objective Particle Swarm Optimization (MOPSO) Method are two popular approaches for addressing multi-objective problems. LWM suffers from strong subjectivity, making it challenging to ensure that each objective is adequately optimized. Meanwhile, MOPSO demands substantial computational resources, is prone to getting trapped in local optima, and its convergence accuracy as well as the uniformity of solution set distribution remain areas for improvement. In the context of inverse design for photonic devices, the problem of dual objectives is particularly prevalent. By employing a two-step inverse design method, we efficiently designed a 50% : 50% directional coupler tailored for operation in the O-band within a relatively short timeframe, requiring only two iterations of a conventional Particle Swarm Optimization algorithm(PSO). This coupler boasts an exceptionally low Insertion Loss(IL) of 0.034dB and a minute Power Splitting Ratio Error (PSRE) of 1.19 × 10⁻⁶. Furthermore, it features a compact footprint of 138um, underscoring its practicality and efficiency.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuanji Zhu, Qi Liu, Liuge Du, and Yi Zou "Research on dual-objective directional coupler based on a two-step inverse design method", Proc. SPIE 13499, AOPC 2024: Optical Devices and Integration, 134990R (13 December 2024); https://doi.org/10.1117/12.3048026

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KEYWORDS

Design

Particle swarm optimization

Directional couplers

Particles

Evolutionary algorithms

Mathematical optimization

Waveguides

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