<p dir="ltr">This thesis presents novel methodologies to integrate AI-driven and physics-inspired methodologies into photonic inverse design, setting new benchmarks for high-performance photonic devices in different branches of photonics. By blending advanced computational techniques with the foundational principles of electromagnetism, this research tackles key challenges in optimizing device efficiency, robustness, and functionality. The aim is to propel photonic technology beyond its current capabilities, offering transformative solutions for a range of novel applications.</p><p dir="ltr">The first major contribution focuses on adjoint-based topology optimization for on-chip single-photon coupling. We developed an adjoint topology optimization scheme to design high-efficiency couplers between photonic waveguides and single-photon sources (SPSs) in hexagonal boron nitride (hBN). This algorithm addresses fabrication constraints and SPS location uncertainties, achieving a remarkable average coupling efficiency of 78%. A library of designs is generated for different positions of the hBN flake containing an SPS relative to a silicon nitride (SiN) waveguide. These designs are then analyzed using dimensionality reduction techniques to investigate the relationship between device geometry and performance, infusing the design process with deep physical intuition and insight.</p><p dir="ltr">The second key advancement is presented through a neural network-based inverse design framework specifically developed for optimizing single-material, variable-index multilayer films. This neural network-driven technique, supported by a differentiable analytical solver, enables the realistic design and fabrication of these multilayer films, achieving high performance under ideal conditions. The approach also addresses the challenge of bridging the gap between these ideal designs and practical devices, which are subject to growth-related imperfections. By incorporating simulated systematic and random errors—reflecting actual deposition challenges—into the optimization process, we demonstrate that the neural network, initially trained to produce the ideal device, can be reconfigured to create designs that compensate for systematic deposition errors. This method remains effective even when random fabrication inconsistencies are present. The results provide a practical and experimentally viable strategy for developing single-material multilayer film stacks, ensuring reliable performance across a wide range of real-world applications.</p><p dir="ltr">The final cornerstone of this research investigates the two-stage inverse design of superchiral dielectric metasurfaces. We propose a two-stage inverse design scheme for dielectric lossless metasurfaces with central superchiral hot spots. By leveraging the excitation of high-quality factor modes with low mode volumes, we achieve up to 19,000-fold enhancements of optical chirality. This method extends the local density of field enhancements for non-chiral fields into the chiral regime and significantly surpasses previous enhancements in superchiral field generation. Our results open new avenues in chiral spectroscopy and chiral quantum photonics, exemplifying the powerful synergy of AI techniques and physics-based design principles in creating highly innovative and functional photonic structures.</p><p dir="ltr">Collectively, the methodologies developed in this thesis signify a major advancement in the field of photonic inverse design. By merging AI-driven techniques with rigorous physics-based optimization frameworks, this research paves the way for the next generation of photonic devices.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/26768077 |
| Date | 19 August 2024 |
| Creators | Omer Yesilurt (19435210) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/_b_PHYSICS_INSPIRED_AI-DRIVEN_PHOTONIC_INVERSE_DESIGN_FOR_HIGH-PERFORMANCE_PHOTONIC_DEVICES_b_/26768077 |
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