More Like this

1. Fast Inverse Design of 3D Nanophotonic Devices Using Boundary Integral Methods

   https://doi.org/10.1021/acsphotonics.2c01072  

   Garza, Emmanuel ; Sideris, Constantine ( October 2022 , ACS Photonics)

   Recent developments in the computational automated design of electromagnetic devices, otherwise known as inverse design, have significantly enhanced the design process for nanophotonic systems. Inverse design can both reduce design time considerably and lead to high-performance, nonintuitive structures that would otherwise have been impossible to develop manually. Despite the successes enjoyed by structure optimization techniques, most approaches leverage electromagnetic solvers that require significant computational resources and suffer from slow convergence and numerical dispersion. Recently, a fast simulation and boundary-based inverse design approach based on boundary integral equations was demonstrated for two-dimensional nanophotonic problems. In this work, we introduce a new full-wave three-dimensional simulation and boundary-based optimization framework for nanophotonic devices also based on boundary integral methods, which achieves high accuracy even at coarse mesh discretizations while only requiring modest computational resources. The approach has been further accelerated by leveraging GPU computing, a sparse block-diagonal preconditioning strategy, and a matrix-free implementation of the discrete adjoint method. As a demonstration, we optimize three different devices: a 1:2 1550 nm power splitter and two nonadiabatic mode-preserving waveguide tapers. To the best of our knowledge, the tapers, which span 40 wavelengths in the silicon material, are the largest silicon photonic waveguiding devices to have been optimized using full-wave 3D solution of Maxwell’s equations. 

   more » « less
2. Methods for tuning plasmonic and photonic optical resonances in high surface area porous electrodes

   https://doi.org/10.1038/s41598-021-86813-y  

   Otto, Lauren M. ; Gaulding, E. Ashley ; Chen, Christopher T. ; Kuykendall, Tevye R. ; Hammack, Aeron T. ; Toma, Francesca M. ; Ogletree, D. Frank ; Aloni, Shaul ; Stadler, Bethanie J. H. ; Schwartzberg, Adam M. ( April 2021 , Scientific Reports)

   Surface plasmons have found a wide range of applications in plasmonic and nanophotonic devices. The combination of plasmonics with three-dimensional photonic crystals has enormous potential for the efficient localization of light in high surface area photoelectrodes. However, the metals traditionally used for plasmonics are difficult to form into three-dimensional periodic structures and have limited optical penetration depth at operational frequencies, which limits their use in nanofabricated photonic crystal devices. The recent decade has seen an expansion of the plasmonic material portfolio into conducting ceramics, driven by their potential for improved stability, and their conformal growth via atomic layer deposition has been established. In this work, we have created three-dimensional photonic crystals with an ultrathin plasmonic titanium nitride coating that preserves photonic activity. Plasmonic titanium nitride enhances optical fields within the photonic electrode while maintaining sufficient light penetration. Additionally, we show that post-growth annealing can tune the plasmonic resonance of titanium nitride to overlap with the photonic resonance, potentially enabling coupled-phenomena applications for these three-dimensional nanophotonic systems. Through characterization of the tuning knobs of bead size, deposition temperature and cycle count, and annealing conditions, we can create an electrically- and plasmonically-active photonic crystal as-desired for a particular application of choice.

    

   more » « less
3. Spatial Iterative Learning Control for Multi-material Three-Dimensional Structures

   https://doi.org/10.1115/1.4046576  

   Afkhami, Zahra ; Pannier, Christopher ; Aarnoudse, Leontine ; Hoelzle, David ; Barton, Kira ( January 2021 , ASME Letters in Dynamic Systems and Control)

   Abstract Iterative learning control (ILC) is a powerful technique to regulate repetitive systems. Additive manufacturing falls into this category by nature of its repetitive action in building three-dimensional structures in a layer-by-layer manner. In literature, spatial ILC (SILC) has been used in conjunction with additive processes to regulate single-layer structures with only one class of material. However, SILC has the unexplored potential to regulate additive manufacturing structures with multiple build materials in a three-dimensional fashion. Estimating the appropriate feedforward signal in these structures can be challenging due to iteration varying initial conditions, system parameters, and surface interaction dynamics in different layers of multi-material structures. In this paper, SILC is used as a recursive control strategy to iteratively construct the feedforward signal to improve part quality of 3D structures that consist of at least two materials in a layer-by-layer manner. The system dynamics are approximated by discrete 2D spatial convolution using kernels that incorporate in-layer and layer-to-layer variations. We leverage the existing SILC models in literature and extend them to account for the iteration varying uncertainties in the plant model to capture a more reliable representation of the multi-material additive process. The feasibility of the proposed diagonal framework was demonstrated using simulation results of an electrohydrodynamic jet printing (e-jet) printing process. 

   more » « less
4. Joint Characterization of Sentinel-2 Reflectance: Insights from Manifold Learning

   https://doi.org/10.3390/rs14225688  

   Sousa, Daniel ; Small, Christopher ( November 2022 , Remote Sensing)

   Most applications of multispectral imaging are explicitly or implicitly dependent on the dimensionality and topology of the spectral mixing space. Mixing space characterization refers to the identification of salient properties of the set of pixel reflectance spectra comprising an image (or compilation of images). The underlying premise is that this set of spectra may be described as a low dimensional manifold embedded in a high dimensional vector space. Traditional mixing space characterization uses the linear dimensionality reduction offered by Principal Component Analysis to find projections of pixel spectra onto orthogonal linear subspaces, prioritized by variance. Here, we consider the potential for recent advances in nonlinear dimensionality reduction (specifically, manifold learning) to contribute additional useful information for multispectral mixing space characterization. We integrate linear and nonlinear methods through a novel approach called Joint Characterization (JC). JC is comprised of two components. First, spectral mixture analysis (SMA) linearly projects the high-dimensional reflectance vectors onto a 2D subspace comprising the primary mixing continuum of substrates, vegetation, and dark features (e.g., shadow and water). Second, manifold learning nonlinearly maps the high-dimensional reflectance vectors into a low-D embedding space while preserving manifold topology. The SMA output is physically interpretable in terms of material abundances. The manifold learning output is not generally physically interpretable, but more faithfully preserves high dimensional connectivity and clustering within the mixing space. Used together, the strengths of SMA may compensate for the limitations of manifold learning, and vice versa. Here, we illustrate JC through application to thematic compilations of 90 Sentinel-2 reflectance images selected from a diverse set of biomes and land cover categories. Specifically, we use globally standardized Substrate, Vegetation, and Dark (S, V, D) endmembers (EMs) for SMA, and Uniform Manifold Approximation and Projection (UMAP) for manifold learning. The value of each (SVD and UMAP) model is illustrated, both separately and jointly. JC is shown to successfully characterize both continuous gradations (spectral mixing trends) and discrete clusters (land cover class distinctions) within the spectral mixing space of each land cover category. These features are not clearly identifiable from SVD fractions alone, and not physically interpretable from UMAP alone. Implications are discussed for the design of models which can reliably extract and explainably use high-dimensional spectral information in spatially mixed pixels—a principal challenge in optical remote sensing.

    

   more » « less
5. Interpreting detrital modes and geochemistry of sandstones from the late Paleozoic Tepuel-Genoa Basin: Paleogeographic implications (Patagonia, Argentina)

   https://doi.org/https://doi.org/10.1016/j.jsames.2020.102858  

   Ciccioli, P. L. ; Limarino, C. O. ; Isbell, J.L. ; Taboada, A. C. ; Pagani, M. A. ; Gulbranson, E.L. ( September 2020 , Journal of South American earth sciences)

   null (Ed.)

   The provenance of sandstones deposited in the late Paleozoic Tepuel-Genoa Basin is analyzed in this paper. Five sections were sampled in Esquel, Sierra de Tepuel, Sierra de Tecka, El Molle, and Río Genoa areas for petrographic and geochemical studies. The sandstones in the Tepuel-Genoa Basin are dominated by feldspathic litharenites and litharenites, showing lithic fragments of volcanic and sedimentary rocks in the Valle Chico Formation and medium-to high-grade metamorphic rock clasts in the rest of the units. Detrital modes of seventy-five sandstones samples from the Valle Chico, Pampa de Tepuel, Moj´on de Hierro, and Río Genoa formations were counted and analyzed. Seven modal components have discriminant value for identifying provenance areas (Qm, Qi, Lv, Lmm-h, Lm-Lp, Lm, Qpm). These modal components allow identification of three petrofacies: 1. Quartzose-lithic (Qm69Lv2Lm29), 2. Quartzose (Qm89Lv4Lm7) and 3. Volcanic-sedimentary (Qm60Lv38Lm1). The quartzose-lithic petrofacies is mainly composed of monocrystalline quartz, medium- and high-grade metamorphic clasts and polycrystalline quartz with cataclastic texture, this assemblage is interpreted as being derived from the crystalline rocks that form the Deseado Massif. The quartzose petrofacies is composed of monocrystalline quartz with scarce contributions of metamorphic clasts and traces of volcanic fragments; the provenance area is ascribed to sedimentary terrains, which most likely covered part of the Deseado Massif. The volcanic-sedimentary petrofacies is comprised of volcanic (acidic and intermediate rocks) and sedimentary (sandstone and mudstone) clasts, with discrete amounts of quartz grains with idiomorph shapes and embayments. This assemblage may correspond to material supply from the Devonian-Early Carboniferous accretionary complex developed in Chile or the unroofing of the western volcanic arc located in the central part of Patagonia. The validity of the three defined petrofacies was evaluated using Principal Component Analysis and triangular compositional diagrams; both methods show good separation and lack of overlap between the three petrofacies. Major (Si, Al, Fe, Na, K) and trace-REE elements (Zr, Th, Sc, Hf) were used to improve the petrographic information. The relation SiO2 against K2O/Na2O indicates that the Pampa de Tepuel and the Moj´on de Hierro formations correspond to a passive margin, while the Valle Chico and Río Genoa formations represent different types of active continental margins. The Th/Sc and Zr/Sc ratios and the Th-Hf-Co distributions indicate that the sandstones of the Tepuel Group were formed from rocks compatibles with the average composition of the upper continental crust. 

   more » « less