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Integrated optical phased arrays (OPAs) have enabled cutting-edge applications where optical beam steering can benefit from chip-scale integration. However, the majority of integrated OPA demonstrations to date have been limited to showing far-field beam forming and steering. There are, however, many emerging applications of integrated photonics where emission of focused light from a chip is desirable, such as in integrated optical tweezers for biophotonics, chip-based 3D printers, and trapped-ion quantum systems. To address this need, we have recently demonstrated the first near-field-focusing integrated OPAs; however, this preliminary demonstration was limited to emission at only one focal plane above the chip. In this paper, we show the first, to the best of our knowledge, spiral integrated OPAs, enabling emission of focusing beams with tunable variable focal heights for the first time. In the process, we develop the theory, explore the design parameters, and propose feed-structure architectures for such OPAs. Finally, we experimentally demonstrate an example spiral integrated OPA system fabricated in a standard silicon-photonics process, showing wavelength-tunable variable-focal-height focusing emission. This work introduces a first-of-its-kind integrated OPA architecture not previously explored or demonstrated in literature and, as such, enables new functionality for emerging applications of OPAs that require focusing operation.

 

Imagine if it were possible to create 3D objects in the palm of your hand within seconds using only a single photonic chip. Although 3D printing has revolutionized the way we create in nearly every aspect of modern society, current 3D printers rely on large and complex mechanical systems to enable layer-by-layer addition of material. This limits print speed, resolution, portability, form factor, and material complexity. Although there have been recent efforts in developing novel photocuring-based 3D printers that utilize light to transform matter from liquid resins to solid objects using advanced methods, they remain reliant on bulky and complex mechanical systems. To address these limitations, we combine the fields of silicon photonics and photochemistry to propose the first chip-based 3D printer. The proposed system consists of only a single millimeter-scale photonic chip without any moving parts that emits reconfigurable visible-light holograms up into a simple stationary resin well to enable non-mechanical 3D printing. Furthermore, we experimentally demonstrate a stereolithography-inspired proof-of-concept version of the chip-based 3D printer using a visible-light beam-steering integrated optical phased array and visible-light-curable resin, showing 3D printing using a chip-based system for the first time. This work demonstrates the first steps towards a highly-compact, portable, and low-cost solution for the next generation of 3D printers.

 

In this work, we design and experimentally demonstrate the first, to the best of our knowledge, integrated polarization splitters and rotators at blue wavelengths. We develop compact and efficient designs for both a polarization splitter and rotator at a 422-nm wavelength, an important laser-cooling transition for⁸⁸Sr⁺ions. These devices are fabricated in a 200-mm wafer-scale process and experimentally demonstrated, resulting in a measured polarization-splitter transverse-electric thru-port coupling of 98.0% and transverse-magnetic tap-port coupling of 77.6% for a compact 16-µm-long device and a polarization-rotator conversion efficiency of 92.2% for a separate compact 111-µm-long device. This work paves the way for more sophisticated integrated control of trapped-ion and neutral-atom quantum systems.

 

We design and experimentally demonstrate the first pair of integrated TE- and TM- emitting gratings at a wavelength of 422nm, targeting the 5²s_1/2-5²p_1/2transition of⁸⁸Sr⁺ions, to enable operations requiring diverse polarizations for integrated-photonics-based trapped-ion quantum systems.

 

Integrated polarization rotators and splitters are designed for the first time at visible wavelengths. Specifically, an adiabatic polarization rotator, an off-axis polarization rotator, and a mode-coupling polarization splitter are designed in a silicon-nitride platform.

 

We develop a framework for two advanced trapped-ion cooling schemes, polarization-gradient and electromagnetically-induced-transparency cooling, for⁸⁸Sr⁺ions using a visible-wavelength integrated-photonics platform and present the design of the key integrated devices.

 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages. 

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.