We present a global optimizer, based on a conditional generative neural network, which can output ensembles of highly efficient topology-optimized metasurfaces operating across a range of parameters. ...A key feature of the network is that it initially generates a distribution of devices that broadly samples the design space and then shifts and refines this distribution toward favorable design space regions over the course of optimization. Training is performed by calculating the forward and adjoint electromagnetic simulations of outputted devices and using the subsequent efficiency gradients for backpropagation. With metagratings operating across a range of wavelengths and angles as a model system, we show that devices produced from the trained generative network have efficiencies comparable to or better than the best devices produced by adjoint-based topology optimization, while requiring less computational cost. Our reframing of adjoint-based optimization to the training of a generative neural network applies generally to physical systems that can utilize gradients to improve performance.
The data-science revolution is poised to transform the way photonic systems are simulated and designed. Photonic systems are, in many ways, an ideal substrate for machine learning: the objective of ...much of computational electromagnetics is the capture of nonlinear relationships in high-dimensional spaces, which is the core strength of neural networks. Additionally, the mainstream availability of Maxwell solvers makes the training and evaluation of neural networks broadly accessible and tailorable to specific problems. In this Review, we show how deep neural networks, configured as discriminative networks, can learn from training sets and operate as high-speed surrogate electromagnetic solvers. We also examine how deep generative networks can learn geometric features in device distributions and even be configured to serve as robust global optimizers. Fundamental data-science concepts framed within the context of photonics are also discussed, including the network-training process, delineation of different network classes and architectures, and dimensionality reduction.Neural networks can capture nonlinear relationships in high-dimensional spaces and are powerful tools for photonic-system modelling. This Review discusses how deep neural networks can serve as surrogate electromagnetic solvers, inverse modelling tools and global device optimizers.
Chemical looping methane partial oxidation provides an energy and cost effective route for methane utilization. However, there is considerable CO
co-production in current chemical looping systems, ...rendering a decreased productivity in value-added fuels or chemicals. In this work, we demonstrate that the co-production of CO
can be dramatically suppressed in methane partial oxidation reactions using iron oxide nanoparticles embedded in mesoporous silica matrix. We experimentally obtain near 100% CO selectivity in a cyclic redox system at 750-935 °C, which is a significantly lower temperature range than in conventional oxygen carrier systems. Density functional theory calculations elucidate the origins for such selectivity and show that low-coordinated lattice oxygen atoms on the surface of nanoparticles significantly promote Fe-O bond cleavage and CO formation. We envision that embedded nanostructured oxygen carriers have the potential to serve as a general materials platform for redox reactions with nanomaterials at high temperatures.
Metasurfaces are subwavelength-structured artificial media that can shape and localize electromagnetic waves in unique ways. The inverse design of these devices is a non-convex optimization problem ...in a high dimensional space, making global optimization a major challenge. We present a new type of population-based global optimization algorithm for metasurfaces that is enabled by the training of a generative neural network. The loss function used for backpropagation depends on the generated pattern layouts, their efficiencies, and efficiency gradients, which are calculated by the adjoint variables method using forward and adjoint electromagnetic simulations. We observe that the distribution of devices generated by the network continuously shifts towards high performance design space regions over the course of optimization. Upon training completion, the best generated devices have efficiencies comparable to or exceeding the best devices designed using standard topology optimization. Our proposed global optimization algorithm can generally apply to other gradient-based optimization problems in optics, mechanics, and electronics.
Dielectric metasurfaces are ultra-thin devices that can shape optical wavefronts with extreme control. While an assortment of materials possessing a wide range of dielectric constants have been ...proposed and implemented, the minimum dielectric contrast required for metasurfaces to achieve high-efficiency performance, for a given device function and feature size constraint, is unclear. In this Article, we examine the impact of dielectric material selection on metasurface efficiency at optical frequencies. As a model system, we design transmissive, single-layer periodic metasurfaces (i.e., metagratings) using topology optimization, and we sweep device thickness and light deflection angle for differing material types. We find that for modest deflection angles below 40 degrees, materials with relatively low dielectric constants near 4 can be used to produce metagratings with efficiencies over 80%. However, ultra-high-efficiency devices designed for large deflection angles and multiple functions require materials with high dielectric constants comparable to silicon. We also identify, for all materials, a minimum device thickness required for optimal metagrating performance that scales inversely with dielectric constant. Our work presents materials selection guidelines for high-performance metasurfaces operating at visible and infrared wavelengths.
Metamaterials and metasurfaces represent a remarkably versatile platform for light manipulation, biological and chemical sensing, and nonlinear optics. Many of these applications rely on the resonant ...nature of metamaterials, which is the basis for extreme spectrally selective concentration of optical energy in the near field. In addition, metamaterial-based optical devices lend themselves to considerable miniaturization because of their subwavelength features. This additional advantage sets metamaterials apart from their predecessors, photonic crystals, which achieve spectral selectivity through their long-range periodicity. Unfortunately, spectral selectivity of the overwhelming majority of metamaterials that are made of metals is severely limited by high plasmonic losses. Here we propose and demonstrate Fano-resonant all-dielectric metasurfaces supporting optical resonances with quality factors Q>100 that are based on CMOS-compatible materials: silicon and its oxide. We also demonstrate that these infrared metasurfaces exhibit extreme planar chirality, opening exciting possibilities for efficient ultrathin circular polarizers and narrow-band thermal emitters of circularly polarized radiation.
The BOLD contrast mechanism has a complex relationship with functional brain activity, oxygen metabolism, and neurovascular factors. Accurate interpretation of the BOLD signal for neuroscience and ...clinical applications necessitates a clear understanding of the sources of BOLD contrast and its relationship to underlying physiology. This review describes the physiological components that contribute to the BOLD signal and the steady-state calibrated BOLD models that enable quantification of functional changes with a separate challenge paradigm. The principles derived from these biophysical models are then used to interpret BOLD measurements in different neurological disorders in the presence of confounding vascular factors related to disease.
•Physiological sources of the BOLD fMRI contrast.•Dynamic and steady-state biophysical models of the BOLD signal.•Baseline oxygenation models (calibrated, qBOLD and fingerprinting).•BOLD signal and models in disease.
We perform
ab initio
DFT+
U
calculations and experimental studies of the partial oxidation of methane to syngas on iron oxide oxygen carriers to elucidate the role of oxygen vacancies in oxygen ...carrier reactivity. In particular, we explore the effect of oxygen vacancy concentration on sequential processes of methane dehydrogenation, and oxidation with lattice oxygen. We find that when CH
4
adsorbs onto Fe atop sites without neighboring oxygen vacancies, it dehydrogenates with CH
x
radicals remaining on the same site and evolves into CO
2
via
the complete oxidation pathway. In the presence of oxygen vacancies, on the other hand, the formed methyl (CH
3
) prefers to migrate onto the vacancy site while the H from CH
4
dehydrogenation remains on the original Fe atop site, and evolves into CO
via
the partial oxidation pathway. The oxygen vacancies created in the oxidation process can be healed by lattice oxygen diffusion from the subsurface to the surface vacancy sites, and it is found that the outward diffusion of lattice oxygen atoms is more favorable than the horizontal diffusion on the same layer. Based on the proposed mechanism and energy profile, we identify the rate-limiting steps of the partial oxidation and complete oxidation pathways. Also, we find that increasing the oxygen vacancy concentration not only lowers the barriers of CH
4
dehydrogenation but also the cleavage energy of Fe-C bonds. However, the barrier of the rate-limiting step cannot further decrease when the oxygen vacancy concentration reaches 2.5%. The fundamental insight into the oxygen vacancy effect on CH
4
oxidation with iron oxide oxygen carriers can help guide the design and development of more efficient oxygen carriers and CLPO processes.
We found that oxygen vacancies can promote CH
4
partial oxidation on iron oxide oxygen carriers during the chemical looping process.
Optimization techniques have been indispensable for designing high-performance meta-devices targeted to a wide range of applications. In fact, today optimization is no longer an afterthought and is a ...fundamental tool for many optical and RF designers. Still, many devices presented in recent literature do not take advantage of optimization techniques. This paper seeks to address this by presenting both an introduction to and a review of several of the most popular techniques currently used for meta-device design. Additionally, emerging techniques like topology optimization and multi-objective optimization and their context to device design are thoroughly discussed. Moreover, attention is given to future directions in meta-device optimization such as surrogate-modeling and deep learning which have the potential to disrupt the fields of optical and radio frequency (RF) inverse-design. Finally, many design examples from the literature are presented and a flow-chart that provides guidance on how best to apply these optimization algorithms to a given problem is provided for the reader.
The growing maturity of nanofabrication has ushered massive sophisticated optical structures available on a photonic chip. The integration of subwavelength-structured metasurfaces and metamaterials ...on the canonical building block of optical waveguides is gradually reshaping the landscape of photonic integrated circuits, giving rise to numerous meta-waveguides with unprecedented strength in controlling guided electromagnetic waves. Here, we review recent advances in meta-structured waveguides that synergize various functional subwavelength photonic architectures with diverse waveguide platforms, such as dielectric or plasmonic waveguides and optical fibers. Foundational results and representative applications are comprehensively summarized. Brief physical models with explicit design tutorials, either physical intuition-based design methods or computer algorithms-based inverse designs, are cataloged as well. We highlight how meta-optics can infuse new degrees of freedom to waveguide-based devices and systems, by enhancing light-matter interaction strength to drastically boost device performance, or offering a versatile designer media for manipulating light in nanoscale to enable novel functionalities. We further discuss current challenges and outline emerging opportunities of this vibrant field for various applications in photonic integrated circuits, biomedical sensing, artificial intelligence and beyond.
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