Title:  A New Paradigm for Knowledge Discovery and Design in Nanophotonics Based on Artificial Intelligence

Committee:

Dr. Ali Adibi, ECE, Chair , Advisor

Dr. Hua Wang, ECE

Dr. Stephen Ralph, ECE

Dr. Benjamin Klein, ECE

Dr. Haomin Zhou, Math

Abstract: The design of photonic devices in the nanoscale regime outperforming the bulky optical components has been a long-lasting challenge in state-of-the-art applications. Accordingly, devising a comprehensive model to understand and explain the physics and dynamics of light-matter interaction in these nanostructures is a substantial step toward realizing novel photonic devices. This thesis presents a new paradigm based on leveraging the intelligent aspect of artificial intelligence (AI) to design nanostructure and understand the underlying physics of light-matter interactions. Considering a large number of design parameters and the complex and non-unique nature of the input-output relations in nanophotonic structures, conventional approaches cannot be used for their design and analysis. The dimensionality reduction (DR) techniques in this research considerably reduce the computing requirements. This thesis also focuses on developing a reliable inverse design approach by overcoming the non-uniqueness challenge. This thesis presents a double-step DR technique to reduce the complexity of the inverse design problem while preserving the necessary information for finding the optimum nanostructure for the desired functionality. I established an approach based on defining physics-driven metrics to explore the low-dimensional manifold of design-response space and provide a sweet region in the reduced design space for the desired functionality. In the later part of the thesis, we have shown that we achieved the optimum nanostructure for a particular desired response by employing manifold learning while minimizing the geometrical complexity. Also, in this thesis, we have developed a manifold learning-based technique for accelerating the design of nanostructures focusing on selecting the optimum material and geometric parameters.