Showing papers by "Jens H. Schmid published in 2018"
TL;DR: How optical metamaterials are expected to enhance the performance of the next generation of integrated photonic devices is reviewed, and some of the challenges encountered in the transition from concept demonstration to viable technology are explored.
Abstract: In the late nineteenth century, Heinrich Hertz demonstrated that the electromagnetic properties of materials are intimately related to their structure at the subwavelength scale by using wire grids with centimetre spacing to manipulate metre-long radio waves. More recently, the availability of nanometre-scale fabrication techniques has inspired scientists to investigate subwavelength-structured metamaterials with engineered optical properties at much shorter wavelengths, in the infrared and visible regions of the spectrum. Here we review how optical metamaterials are expected to enhance the performance of the next generation of integrated photonic devices, and explore some of the challenges encountered in the transition from concept demonstration to viable technology.
585 citations
TL;DR: In this article, a machine-learning-based approach is used to map and characterize the multi-parameter design space of nanophotonic components, revealing the interplay of the design parameters, highlighting performance and structural limitations.
Abstract: Nanophotonics finds ever broadening applications requiring complex component designs with a large number of parameters to be simultaneously optimized. Recent methodologies employing optimization algorithms commonly focus on a single design objective, provide isolated designs, and do not describe how the design parameters influence the device behaviour. Here we propose and demonstrate a machine-learning-based approach to map and characterize the multi-parameter design space of nanophotonic components. Pattern recognition is used to reveal the relationship between an initial sparse set of optimized designs through a significant reduction in the number of characterizing parameters. This defines a design sub-space of lower dimensionality that can be mapped faster by orders of magnitude than the original design space. As a result, multiple performance criteria are clearly visualized, revealing the interplay of the design parameters, highlighting performance and structural limitations, and inspiring new design ideas. This global perspective on high-dimensional design problems represents a major shift in how modern nanophotonic design is approached and provides a powerful tool to explore complexity in next-generation devices.
66 citations