Dielectric metalenses realized by economic photolithography technology are vital to their mass deployment in optoelectronic applications. However, pattern fidelity has become a serious issue that degrades the device performance due to optical proximity effects. Here, we demonstrate an intelligent reticle modification system which modifies the sizes and shapes of designed patterns based on a neural-network U-net lithographic model to produce nanostructures with desired dimensions. We demonstrate 2 mm-diameter visible metalenses with diffraction-limited focusing using DUV KrF 248 nm photolithography. This work bridges between the semiconductor process and lens-making industries to realize high-volume manufacturing of versatile metalens and metasurface products.
In this study, we propose a deep-learning approach to establish the lithographic model for i-line photolithography and develop an optical proximity correction (OPC) algorithm to increase the resolution limit. The applications of RETs are not only on CMOS semiconductor, but also on some metasurface which used to patterning by electron beam lithography. With the OPC algorithm, we are able to manufacture a near-infrared metalens patterning by i-line photolithography in a more efficient and less expensive way.
Solid-state beam steering is the key to realize miniature, mass-producible LIDAR (Light Detection And Ranging) and freespace communication systems without using any moving parts. The huge power consumption required in solid-state beam steering, however, prevents this technology from further scaling. Here we show two different approaches to enable lowpower solid-state beam steering. In the first approach, we use spatial-mode multiplexing to reduce the power consumption of the phase shifters in a large-scale optical phased array. We show an improvement of phase shifter power consumption by nearly 9 times, without sacrificing optical bandwidth or operation speed. Using this approach, we demonstrate 2D beam steering with a silicon photonic phased array containing 512 actively controlled elements. This phased array consumes only 1.9 W of power while steering over a 70° × 6° field of view. This power consumption is at least an order of magnitude lower compared to other demonstrated large-scale active phased arrays. In the second approach, we achieve 2D beam steering with a switchable emitter array and a metalens that collimates the emitted light. The power consumption of this approach scales logarithmically with the number of emitters and therefore favors large-scale systems. This approach allows straightforward feedback control and better robustness to environmental temperature change. Our approaches demonstrate a path forward to build truly scalable beam steering devices.
Graphene is one of the emerging active nanophotonics materials with optical properties that can be controlled in real time by an applied bias voltage. Different applications from sensing to active nanophotonics have been discussed in the literature recently and the field is still developing especially with an eye on structured and multi-layer graphene. To design new devices there is a need for precise modeling of multivariate and dynamic optical responses of graphene elements in frequency and time domains. Taking into account the complexity that comes along with multiple unknown parameters, including edge effects in nanostructured graphene elements, graphene impurities, imperfections of characterization optics etc., it is hard to build an adequate multivariate model to reach good quantitative agreement with experiment.
Here, we present an approach that uses optimization methods to retrieve the optical properties of a given graphene sample from experiment. We show that with these techniques good quantitative agreement with experiments can be achieved; additionally we encapsulate our techniques in an online data-fitting tool. The tool includes several options to precisely fit the conductivity function to a given experiment - general spline approximations and physically meaningful random phase approximation models for frequency domain solvers, along with the relaxed Lorentz oscillator models for confident time domain simulations. A pilot version of our free online tool entitled Photonics2D-Fit (to be staged at nanoHUB.org) is presented.
Graphene-based hyperbolic metamaterials (HMMs) enable new possibilities that are not attainable with conventional metal-based HMMs, such as tunability of optical properties and the ability to combine with graphene-based photodetection. A graphene HMM is made of alternating graphene-dielectric multilayers, whose properties can be understood with the effective-medium approximation (EMA). The initial experimental realization of this novel metamaterial has been demonstrated with a far-field measurement, and in this paper we investigate the light coupling from free space into a graphene HMM slab with a metallic grating using numerical simulations. We show that light can be efficiently coupled into the high-k guided modes in the HMM slab and be absorbed by the graphene layers, which can be applied to create ultrathin super absorbers.
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