Inference of process variations in silicon photonics from characterization measurements.

Understanding process variations and their impact in silicon photonics remains challenging. To achieve high-yield manufacturing, a key step is to extract the magnitude and spatial distribution of process variations in the actual fabrication, which is usually based on measurements of replicated test structures. In this paper, we develop a Bayesian-based method to infer the distribution of systematic geometric variations in silicon photonics, without requiring replication of identical test structures. We apply this method to characterization data from multiple silicon nitride ring resonators with different design parameters. We extract distributions with standard deviation of 28 nm, 0.8 nm, and 3.8 nm for the width, thickness, and partial etch depth, respectively, as well as the spatial maps of these variations. Our results show that this characterization and extraction approach can serve as an efficient method to study process variation in silicon photonics, facilitating the design of high-yield silicon photonic circuits in the future.

Illumination of dense urban areas by light redirecting panels.

With the high population growth rate, especially in developing countries, and the scarcity of land resources, buildings are becoming so close to each other, depriving the lower floors and the alleys from sunlight and consequently causing health problems. Therefore, there is an urgent need for cost-effective efficient light redirecting panels that guide sun rays into those dim places. In this paper, we address this problem. A novel sine wave based panel is presented to redirect/diverge light downward and enhance the illumination level in those dark places. Simulation results show that the proposed panel improves the illuminance values by more than 200% and 400% in autumn and winter respectively, operates over wide solar altitude ranges, and redirects light efficiently. Experimental and simulation results are in good agreement.