arXiv subjects

Jonathan A. Fan

Publications and source records attributed to Jonathan A. Fan.

28 records · Page 2Linked to original sources

arXiv2019

Progressive-Growing of Generative Adversarial Networks for Metasurface Optimization

Generative adversarial networks, which can generate metasurfaces based on a training set of high performance device layouts, have the potential to significantly reduce the computational cost of the metasurface design process. However, basic GAN architectures are unable to fully capture the detailed features of topologically complex metasurfaces, and generated devices therefore require additional computationally-expensive design refinement. In this Letter, we show that GANs can better learn spatially fine features from high-resolution training data by progressively growing its network architecture and training set. Our results indicate that with this training methodology, the best generated devices have performances that compare well with the best devices produced by gradient-based topology optimization, thereby eliminating the need for additional design refinement. We envision that this network training method can generalize to other physical systems where device performance is strongly correlated with fine geometric structuring.

physics.comp-ph↗

arXiv2019

Simulator-based training of generative models for the inverse design of metasurfaces

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.

physics.comp-ph↗

arXiv2019

Global optimization of dielectric metasurfaces using a physics-driven neural network

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 towards 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.

cs.LG↗

arXiv2019

Near 100% CO Selectivity in Nanoscaled Iron-Based Oxygen Carriers for Chemical Looping Methane Partial Oxidation

Chemical looping methane partial oxidation provides an energy and cost effective route for methane utilization. However, there is considerable CO2 co-production in state-of-the-art chemical looping systems, rendering a decreased productivity in value-added fuels or chemicals. In this work, we show that the co-production of CO2 can be dramatically suppressed in methane partial oxidation reactions using iron oxide nanoparticles, with a size of 2~8 nm, as the oxygen carrier. To stabilize these nanoparticles at high temperatures, they are embedded in an ordered, gas-permeable mesoporous silica matrix. We experimentally obtained near 100% CO selectivity in a cyclic redox system at 750°C to 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 reveal that CH4 adsorption energies decrease with increasing nanoparticle size. These calculations also 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 achieving 100% selectivity in redox reactions at high temperatures.

physics.chem-ph↗

arXiv2019

Freeform Diffractive Metagrating Design Based on Generative Adversarial Networks

A key challenge in metasurface design is the development of algorithms that can effectively and efficiently produce high performance devices. Design methods based on iterative optimization can push the performance limits of metasurfaces, but they require extensive computational resources that limit their implementation to small numbers of microscale devices. We show that generative neural networks can train from images of periodic, topology-optimized metagratings to produce high-efficiency, topologically complex devices operating over a broad range of deflection angles and wavelengths. Further iterative optimization of these designs yields devices with enhanced robustness and efficiencies, and these devices can be utilized as additional training data for network refinement. In this manner, generative networks can be trained, with a onetime computation cost, and used as a design tool to facilitate the production of near-optimal, topologically-complex device designs. We envision that such data-driven design methodologies can apply to other physical sciences domains that require the design of functional elements operating across a wide parameter space.

physics.optics↗

arXiv2019

Tunable Hyperbolic Metamaterials Based on Self-Assembled Carbon Nanotubes

We show that packed, horizontally aligned films of single-walled carbon nanotubes are hyperbolic metamaterials with ultra-subwavelength unit cells and dynamic tunability. Using Mueller-matrix ellipsometry, we characterize the films' doping-level dependent optical properties and find a broadband hyperbolic region tunable in the mid-infrared. To characterize the dispersion of in-plane hyperbolic plasmon modes, we etch the nanotube films into nanoribbons with differing widths and orientations relative to the nanotube axis, and we observe that the hyperbolic modes support strong light localization. Agreement between the experiments and theoretical models using the ellipsometry data indicates that the packed carbon nanotubes support bulk anisotropic responses at the nanoscale. Self-assembled films of carbon nanotubes are well suited for applications in thermal emission and photodetection, and they serve as model systems for studying light-matter interactions in the deep subwavelength regime.

physics.optics↗

arXiv2017

Freeform metagratings based on complex light scattering dynamics for extreme, high efficiency beam steering

Conventional phased-array metasurfaces utilize resonant nanoparticles or nanowaveguides to specify spatially-dependent amplitude and phase responses to light. In nearly all these implementations, subwavelength-scale elements are stitched together while minimizing coupling between adjacent elements. In this report, we theoretically analyze an alternate method of metasurface design, utilizing freeform inverse design methods, which support significantly enhanced efficiencies compared to conventional designs. Our design process optimizes wavelength-scale elements, which dramatically increases the design space for optical engineering. An in-depth coupled mode analysis of ultra-wide-angle beam deflectors and wavelength splitters shows that the extraordinary performance of our designs originates from the large number of propagating modes supported by the metagrating, in combination with complex multiple scattering dynamics exhibited by these modes. We also apply our coupled mode analysis to conventional nanowaveguide-based metasurfaces to understand and quantify the factors limiting the efficiencies of these devices. We envision that freeform metasurface design methods will open new avenues towards truly high-performance, multi-functional optics by utilizing strongly coupled nanophotonic modes and elements.

physics.optics↗

arXiv2013

Silicon-based Infrared Metamaterials with Ultra-Sharp Fano Resonances

Metamaterials and meta-surfaces represent a remarkably versatile platform for light manipulation, biological and chemical sensing, nonlinear optics, and even spaser lasing. 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. The simplicity of free-space light coupling into sharply-resonant meta-surfaces with high resonance quality factors Q>>1 is a significant practical advantage over the extremely angle-sensitive diffractive structures or inherently inhomogeneous high-Q photonic structures such as toroid or photonic crystal microcavities. Such spectral selectivity is presently impossible for the overwhelming majority of metamaterials that are made of metals and suffer from high plasmonic losses. Here, we propose and demonstrate Fano-resonant all-semiconductor optical meta-surfaces supporting optical resonances with quality factors Q>100 that are almost an order of magnitude sharper than those supported by their plasmonic counterparts. These silicon-based structures are shown to be planar chiral, opening exciting possibilities for efficient ultra-thin circular polarizers and narrow-band thermal emitters of circularly polarized radiation.

physics.optics↗

arXiv2012

Tetrahedral colloidal clusters from random parking of bidisperse spheres

Using experiments and simulations, we investigate the clusters that form when colloidal spheres stick irreversibly to -- or "park" on -- smaller spheres. We use either oppositely charged particles or particles labeled with complementary DNA sequences, and we vary the ratio $α$ of large to small sphere radii. Once bound, the large spheres cannot rearrange, and thus the clusters do not form dense or symmetric packings. Nevertheless, this stochastic aggregation process yields a remarkably narrow distribution of clusters with nearly 90% tetrahedra at $α=2.45$. The high yield of tetrahedra, which reaches 100% in simulations at $α=2.41$, arises not simply because of packing constraints, but also because of the existence of a long-time lower bound that we call the "minimum parking" number. We derive this lower bound from solutions to the classic mathematical problem of spherical covering, and we show that there is a critical size ratio $α_c=(1+\sqrt{2})\approx 2.41$, close to the observed point of maximum yield, where the lower bound equals the upper bound set by packing constraints. The emergence of a critical value in a random aggregation process offers a robust method to assemble uniform clusters for a variety of applications, including metamaterials.

cond-mat.soft↗

arXiv2005

Compact Quasi-Chaotic Optical Cavity

A novel, 3-dimensional, convex, multi-pass optical cavity with partially-chaotic ray dynamics is presented. The light is localized near stable, long-path length trajectories supported by the cavity, and beam diffraction is suppressed by the phase space barriers between the regions of regular and chaotic ray dynamics that are generally present in partially-chaotic systems. For a centimeter-size cavity, the design supports meter-scale optical path lengths, suggesting future applications in trace gas detection. An exemplary cavity has been fabricated from a hollow, gold-coated, acrylic shell. Our measurements using a HeNe laser and a pulsed red diode laser for characterization of the cavity beam pattern and optical path length, respectively, confirm the theoretically predicted optical dynamics and the ability of the cavity to support meter-scale path lengths.

physics.optics↗