Ultra-low-loss broadband multiport optical splitters.

A major challenge in inverse design of optical splitters is to efficiently reach platform nonspecific designs constrained to multiple functional requirements: arbitrary splitting ratio, low insertion loss, broad bandwidth and small footprint. While the traditional designs fail to fulfill all these requirements, the more successful nanophotonic inverse designs require substantial time and energy resources per device. Here, we present an efficient inverse design algorithm that provides universal designs of splitters compliant with all above constraints. To demonstrate the capabilities of our method, we design splitters with various splitting ratios and fabricate 1 × N power splitters in a borosilicate platform by direct laser writing. The splitters show zero loss within the experimental error, competitive imbalance of <0.5 dB and broad bandwidth in the range 20 - 60 nm around 640 nm. Remarkably, the splitters can be tuned to achieve different splitting ratios. We further demonstrate scaling of the splitter footprint and apply the universal design to silicon nitride and silicon-on-insulator platforms to achieve 1 × 5 splitters with the footprints as small as 3.3 µm × 8 µm and 2.5 µm × 10.3 µm, respectively. Owing to the universality and speed of the design algorithm (several minutes on a standard PC) our approach renders 100 greater throughput than nanophotonic inverse design.

Bayesian analysis of Ag thin films formation.

A detailed numerical study of the formation of metallic silver thin films ranged from 8 up to 50 nm on thickness is presented. The topography of these films was imaged by Atomic Force Microscopy, and starting from these images, some surface parameters were obtained. We characterized the root mean square roughness evolution by a simple power-law model with a coefficient α=0.74±0.01 consistent with the theoretical results of Family and Vicsek (1985), Family (1990). Additionally, we considered different models to describe the distributions of the grains' heights and sizes, and analyzed them via Bayesian statistics and a Markov Chain Monte Carlo numerical method. This Bayesian analysis has been significantly helpful in this work for allowing the study of the models that represent our data best and considering the experimental errors as instrumental data. The results of this analysis suggest an individual grains' growth followed by a collapse between neighboring grains.