Computationally efficient nanophotonic design through data-driven eigenmode expansion

Silicon photonic components require rapid design procedures with state-of-the-art optical metrics as the on-chip photonic applications advance. In this dissertation, a highly efficient and flexible method is introduced for designing a variety of low-loss waveguides in compact footprints. The proposed data-driven eigenmode expansion method represents waveguides as cascading eigenmode scattering matrices and propagation matrices. This method uses parallel data processing approaches to perform electromagnetic computations for simulating the optical response of individual waveguides in tens of milliseconds, orders of magnitude faster than the conventional methods, while achieving physical accuracies respected to 3D-FDTD. This framework, coupled with nonlinear optimization algorithms, designs adiabatic tapers, power splitters, and waveguide crossings that show near-lossless state-of-the-art operation within broad bandwidths. These devices and their 3D-FDTD simulations and experimental measurements highlight this methodology's capabilities and computational efficiency. A few photonic design problems currently under development will further demonstrate its applicability.