Publications

Light-matter interaction optimization in complex nanophotonic structures is a critical step towards the tailored performance of photonic devices. The increasing complexity of such systems requires new optimization strategies beyond intuitive methods. For example, in disordered photonic structures, the spatial distribution of energy densities has large random fluctuations due to the interference of multiply scattered electromagnetic waves, even though the statistically averaged spatial profiles of the transmission eigenchannels are universal. Classification of these eigenchannels for a single configuration based on visualization of intensity distributions is difficult. However, successful classification could provide vital information about disordered nanophotonic structures. Emerging methods in machine learning have enabled new investigations into optimized photonic structures. In this work, we combine intensity distributions of the transmission eigenchannels and the transmitted speckle-like intensity patterns to classify the eigenchannels of a single configuration of disordered photonic structures using machine learning techniques. Specifically, we leverage supervised learning methods, such as decision trees and fully connected neural networks, to achieve classification of these transmission eigenchannels based on their intensity distributions with an accuracy greater than 99%, even with a dataset including photonic devices of various disorder strengths. Simultaneous classification of the transmission eigenchannels and the relative disorder strength of the nanophotonic structure is also possible. Our results open new directions for machine learning assisted speckle-based metrology and demonstrate a novel approach to classifying nanophotonic structures based on their electromagnetic field distributions. These insights can be of paramount importance for optimizing light-matter interactions at the nanoscale.

Abstract not provided.

The effects of paralleling low-current vertical Gallium Nitride (v-GaN) diodes in a custom power module are reported. Four paralleled v-GaN diodes were demonstrated to operate in a buck converter at 1.3 Apeak (792 mArms) at 240 V and 15 kHz switching frequency. Additionally, high-fidelity SPICE simulations demonstrate the effects of device parameter variation on power sharing in a power module. The device parameters studied were found to have a sub-linear relationship with power sharing, indicating a relaxed need to bin parts for paralleling. This result is very encouraging for power electronics based on low-current v-GaN and demonstrates its potential for use in high-power systems.