Subwavelength Bayer RGB color routers with perfect optical efficiency.

We introduce subwavelength color routers with perfect optical efficiency in a red-green-green-blue (RGGB) Bayer layout for solid state image sensors. This is the first demonstration of a subwavelength device concept that shows the full potential of color routing, i.e.

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.

Theory for Twisted Bilayer Photonic Crystal Slabs.

We analyze scattering properties of twisted bilayer photonic crystal slabs through a high-dimensional plane wave expansion method. The method is applicable for arbitrary twist angles and does not suffer from the limitations of the commonly used supercell approximation. We show strongly tunable resonance properties of this system which can be accounted for semianalytically from a correspondence relation to a simpler structure.

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.

Accelerating adjoint variable method based photonic optimization with Schur complement domain decomposition.

Adjoint variable method in combination with gradient descent optimization has been widely used for the inverse design of nanophotonic devices. In many of such optimizations, the design region is only a small fraction of the total computational domain. Here we show that the adjoint variable method can be combined with the Schur complement domain decomposition method.

Near-complete violation of Kirchhoff's law of thermal radiation with a 0.3 T magnetic field.

The capability to overcome Kirchhoff's law of thermal radiation provides new opportunities in energy harvesting and thermal radiation control. Previously, design towards demonstrating such capability requires a magnetic field of 3 T, which is difficult to achieve in practice. In this work, we propose a nanophotonic design that can achieve such capability with a far more modest magnetic field of 0.

Accelerating convergence of an iterative solution of finite difference frequency domain problems via schur complement domain decomposition.

We show that iterative solution of Maxwell's equations using the finite-difference frequency-domain method can be significantly accelerated by using a Schur complement domain decomposition method. We account for the improvement by analyzing the spectral properties of the linear systems resulting from the use of the domain decomposition method.