Design of compact photonic integrated couplers with evolutionary algorithms for solar cell and on-chip

Coupling is an important aspect of manipulating electromagnetic waves as it involves the transfer of energy between two or more systems via electromagnetic fields. Over the years, humanity has learned to control electromagnetic waves through the development of devices such as waveguides, antennas, and lenses. This has allowed the creation of more efficient and versatile systems for a variety of applications. The need for better solar cells, fiber-to-chip couplers and waveguide-to-waveguide couplers is driven by the ever-increasing demand for sustainable energy sources, faster and more reliable communication networks, and integrated photonic systems. Improved solar cells can contribute to a cleaner energy future, while advanced couplers enable seamless integration of fiber optics and photonic integrated circuits, resulting in more efficient data transmission and powerfull computing. Waveguide to substrate coupler is a device that has not been given much attention until today. Optimizing these couplers can dramatically increase the efficiency and compactness of THz antenna arrays, lensless imaging systems, and lab-on-a-chip. While THz antenna arrays could revolutionize wireless communication and sensing applications, they have the potential to transform lensless imaging, medical diagnostics and materials characterization. On the other hand, labs on a chip will promise to shrink and streamline complex laboratory processes, paving the way for portable and highly integrated diagnostic tools. In summary, devices that provide the coupling of electromagnetic waves in various applications are critical and algorithms have come to the fore in the design of these devices in recent years. Particle swarm optimization and genetic algorithm, which are evolutionary algorithms, provide many advantages compared to traditional design methods. First, photonic devices often have multidimensional parameter spaces. Evolutionary algorithms are well suited to navigating these complex areas as they can effectively search a wide variety of possible solutions. Second, evolutionary algorithms are more likely to find the overall optimum, as opposed to gradient-based optimization methods that can easily stuck to local minimums. Third, particle swarm optimization and genetic algorithms can be easily tailored to specific design constraints and goals. Finally, evolutionary algorithms are inherently resilient to noise and uncertainties in the design process. Taking advantage of the strengths of particle swarm optimization and genetic algorithms, this thesis explains the importance of photonic couplers and the algorithms used in the first part. In the second part, surface shaping for solar cells and fiber to chip coupler designs are given. In the third part, devices that enable the light to be integrated within the chip are designed. In the last part, the results obtained in the thesis are interpreted and the predictions are shared.