The inverse design of optical metasurfaces is a rapidly emerging field that has already shown great promise in miniaturizing conventional optics as well as developing completely new optical functionalities. Such a design process relies on many forward simulations of a device’s optical response in order to optimize its performance. We present a data-driven forward simulation framework for the inverse design of metasurfaces that is more accurate than methods based on the local phase approximation, a factor of 104 times faster and requires 15 times less memory than mesh-based solvers and is not constrained to spheroidal scatterer geometries. We explore the scattered electromagnetic field distribution from wavelength scale cylindrical pillars, obtaining low-dimensional representations of our data via the singular value decomposition. We create a differentiable model fiting the input geometries and configurations of our metasurface scatterers to the low-dimensional representation of the output field. To validate our model, we inverse design two optical elements: a wavelength multiplexed element that focuses light for λ = 633 nm and produces an annular beam at λ = 400 nm and an extended depth of focus lens.

We present an experimental implementation of the recently proposed dual-space microscopy (DSM), an optical microscopy technique based on simultaneous observation of an object in the position and momentum spaces, using computer-controlled hemispherical digital condensers. We demonstrate that DSM is capable of resolving structures below the Rayleigh resolution limit.

Sub-wavelength diffractive optics, commonly known as metasurfaces, have recently garnered significant attention for their ability to create ultra-thin flat lenses with high numerical aperture. Several materials with different refractive indices have been used to create metasurface lenses (metalenses). In this paper, we analyze the role of material refractive indices on the performance of these metalenses. We employ both forward and inverse design methodologies to perform our analysis. We found that, while high refractive index materials allow for extreme reduction of the focal length, for moderate focal lengths and numerical aperture (<0.6), there is no appreciable difference in focal spot-size and focusing efficiency for metalenses made of different materials with refractive indices ranging between n= 1.25 to n=3.5.

As artificial neural networks (ANNs) continue to make strides in wide-ranging and diverse fields of technology, the search for more efficient hardware implementations beyond conventional electronics is gaining traction. In particular, optical implementations potentially offer extraordinary gains in terms of speed and reduced energy consumption due to intrinsic parallelism of free-space optics. At the same time, a physical nonlinearity -a crucial ingredient of an ANN -is not easy to realize in free-space optics, which restricts the potential of this platform. This problem is further exacerbated by the need to perform the nonlinear activation also in parallel for each data point to preserve the benefit of linear free-space optics. Here, we present a free-space optical ANN with diffraction-based linear weight summation and nonlinear activation enabled by the saturable absorption of thermal atoms. We demonstrate, via both simulation and experiment, image classification of handwritten digits using only a single layer and observed 6% improvement in classification accuracy due to the optical nonlinearity compared to a linear model. Our platform preserves the massive parallelism of free-space optics even with physical nonlinearity, and thus opens the way for novel designs and wider deployment of optical ANNs.

No abstract