Efficient method for accelerating line searches in adjoint optimization of photonic devices by combining Schur complement domain decomposition and Born series expansions.

A line search in a gradient-based optimization algorithm solves the problem of determining the optimal learning rate for a given gradient or search direction in a single iteration. For most problems, this is determined by evaluating different candidate learning rates to find the optimum, which can be expensive. Recent work has provided an efficient way to perform a line search with the use of the Shanks transformation of a Born series derived from the Lippman-Schwinger formalism.

Miniature Optical Particle Counter and Analyzer Involving a Fluidic-Optronic CMOS Chip Coupled with a Millimeter-Sized Glass Optical System.

Our latest advances in the field of miniaturized optical PM sensors are presented. This sensor combines a hybrid fluidic-optronic CMOS (holed retina) that is able to record a specific irradiance pattern scattered by an illuminated particle (scattering signature), while enabling the circulation of particles toward the sensing area. The holed retina is optically coupled with a monolithic, millimeter-sized, refracto-reflective optical system.

Publisher Correction: Integrated photonic guided metalens based on a pseudo-graded index distribution.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Determining the optimal learning rate in gradient-based electromagnetic optimization using the Shanks transformation in the Lippmann-Schwinger formalism.

In gradient-based optimization of photonic devices, within the overall design parameter space, one iteratively performs a line search in a one-dimensional subspace as spanned by the search direction. While the search direction can be efficiently determined with the adjoint variable method, there has not been an efficient algorithm that determines the optimal learning rate that controls the distance one moves along the search direction. Here we introduce an efficient algorithm of determining the optimal learning rate, using the Shanks transformation in the Lippmann-Schwinger formalism.

Integrated photonic guided metalens based on a pseudo-graded index distribution.

In this article, we report an integrated optical nanolens exhibiting a pseudo-graded index distribution in a guided configuration. This dielectric metalens relies on a permittivity distribution through dielectric strips of the core material, which is compatible with existing silicon photonic technology. We show in this paper that effective medium theory (EMT) inaccurately predicts the focal length of such devices, and we propose an efficient and accurate design approach based on 2D finite element method (FEM) mode calculations that are in good agreement with 3D FDTD simulations.