Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “MASK”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Dark Energy Survey Year 3 results: Simulation-based cosmological inference with wavelet harmonics, scattering transforms, and moments of weak lensing mass maps. Validation on simulations

Beyond-two-point statistics contain additional information on cosmological as well as astrophysical and observational (systematics) parameters. In this methodology paper we provide an end-to-end simulationbased analysis of a set of Gaussian and non-Gaussian weak lensing statistics using detailed mock catalogs of the Dark Energy Survey (DES). Here, we implement: 1) second and third moments; 2) wavelet phase harmonics (WPH); 3) the scattering transform (ST). Our analysis is fully based on simulations, it spans a space of seven $νw$CDM cosmological parameters, and it forward models the most relevant sources of systematics of the data (masks, noise variations, clustering of the sources, intrinsic alignments, and shear and redshift calibration). We implement a neural network compression of the summary statistics, and we estimate the parameter posteriors using a likelihood-free-inference approach. We validate the pipeline extensively, and we find that WPH exhibits the strongest performance when combined with second moments, followed by ST, and then by third moments. The combination of all the different statistics further enhances constraints with respect to second moments, up to 25 percent, 15 percent, and 90 percent for S 8 , Ω m , and the figure-of-merit FoM S8;Ωm , respectively. We further find that non-Gaussian statistics improve constraints on w and on the amplitude of intrinsic alignment with respect to second moments constraints. The methodological advances presented here are suitable for application to Stage IV surveys from Euclid, Rubin-LSST, and Roman with additional validation on mock catalogs for each survey. In a companion paper we present an application to DES Year 3 data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Dark Energy Survey Year 3 results: Simulation-based cosmological inference with wavelet harmonics, scattering transforms, and moments of weak lensing mass maps. II. cosmological results

Here, we present a simulation-based cosmological analysis using a combination of Gaussian and non-Gaussian statistics of the weak lensing mass (convergence) maps from the first three years of the Dark Energy Survey. We implement the following: (1) second and third moments; (2) wavelet phase harmonics; (3) the scattering transform. Our analysis is fully based on simulations, spans a space of seven 𝑤 Cold Dark Matter (𝑤⁢ CDM) cosmological parameters, and forward models the most relevant sources of systematics inherent in the data: masks, noise variations, clustering of the sources, intrinsic alignments, and shear and redshift calibration. We implement a neural network compression of the summary statistics, and we estimate the parameter posteriors using a simulation-based inference approach. Including and combining different non-Gaussian statistics is a powerful tool that strongly improves constraints over Gaussian statistics (in our case, the second moments); in particular, the figure of merit (𝑆 8 , Ω m ) is improved by 70% (Λ ⁢CDM) and 90% (𝑤 ⁢CDM). When all the summary statistics are combined, we achieve a 2% constraint on the amplitude of fluctuations parameter 𝑆 8 ≡ 𝜎 8 ⁢(Ω m /0.3) 0.5 , obtaining 𝑆 8 = 0.794 ±0.017 (Λ⁢ CDM) and 𝑆 8 = 0.817 ±0.021 (𝑤 ⁢CDM), and a ∼10% constraint on Ω m , obtaining Ω m =0.259 ±0.025 (Λ ⁢CDM) and Ω m = 0.273 ±0.029 (𝑤⁢ CDM). In the context of the 𝑤⁢ CDM scenario, these statistics also strengthen the constraints on the parameter 𝑤, obtaining 𝑤 <−0.72. The constraints from different statistics are shown to be internally consistent (with a 𝑝-value>0.1 for all combinations of statistics examined). We compare our results to other weak lensing results from the first three years of the Dark Energy Survey data, finding good consistency; we also compare with results from external datasets, such as planck constraints from the cosmic microwave background, finding statistical agreement, with discrepancies no greater than <2.2⁢𝜎.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Foreground-Immune Cosmic Microwave Background Lensing with Shear-Only Reconstruction

Cosmic microwave background (CMB) lensing from current and upcoming wide-field CMB experiments such as AdvACT, SPT-3G and Simons Observatory relies heavily on temperature (versus polarization). In this regime, foreground contamination to the temperature map produces significant lensing biases, which cannot be fully controlled by multifrequency component separation, masking, or bias hardening. Here in this Letter, we split the standard CMB lensing quadratic estimator into a new set of optimal “multipole” estimators. On large scales, these multipole estimators reduce to the known magnification and shear estimators, and a new shear B-mode estimator. We leverage the different symmetries of the lensed CMB and extragalactic foregrounds to argue that the shear-only estimator should be approximately immune to extragalactic foregrounds. We build a new method to compute, separately and without noise, the primary, secondary, and trispectrum biases to CMB lensing from foreground simulations. Using this method, we demonstrate that the shear estimator is, indeed, insensitive to extragalactic foregrounds, even when applied to a single-frequency temperature map contaminated with cosmic infrared background, thermal Sunyaev-Zel’dovich, kinematic Sunyaev-Zel’dovich, and radio point sources. This dramatic reduction in foreground biases allows us to include higher temperature multipoles than with the standard quadratic estimator, thus, increasing the total lensing signal-to-noise ratio beyond the quadratic estimator. In addition, magnification-only and shear B-mode estimators provide useful diagnostics for potential residuals.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Diamonds in the rough: a strong case for the inclusion of weak-intensity X-ray diffraction data

Here, new evidence is provided to show that the inclusion of weak-intensity, high-resolution X-ray diffraction data helps to improve the quality of experimental phases by imposing proper constraints on electron-density models during noncrystallographic symmetry averaging. Overwhelming evidence exists to show that the inclusion of weak-intensity, high-resolution X-ray diffraction data helps improve the refinement of atomic models by imposing strong constraints on individual and overall temperature B factors and thus the quality of crystal structures. Some researchers consider these data to be of little value and opt to discard them during data processing, particularly at medium and low resolution, at which individual B factors of atomic models cannot be refined. Here, new evidence is provided to show that the inclusion of these data helps to improve the quality of experimental phases by imposing proper constraints on electron-density models during noncrystallographic symmetry (NCS) averaging. Using electron-density correlation coefficients as criteria, the resolution of data has successfully been extended from 3.1 to 2.5 Å resolution with redundancy-independent merging R factors from below 100% to about 310%. It is further demonstrated that phase information can be fully extracted from observed amplitudes through de novo NCS averaging. Averaging starts with uniform density inside double-shelled spherical masks and NCS matrices that are derived from bound heavy-atom clusters at the vertices of cuboctahedrally symmetric protein particles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A Weakly-Supervised, Multitask Deep Learning Framework for Shadow Mitigation in Remote Sensing Imagery

We propose a weakly-supervised, multitask framework for training a convolutional neural network to solve the problem of cloud shadow mitigation given only cloud and shadow masks as labels. The network minimizes the Wasserstein distance between shadows and their proximal sunlit neighborhoods, generating a supervisory signal directly from within the input image. We extract further utility from the shadow mask through multitask learning by introducing an auxiliary task of shadow segmentation. Our approach is advantageous since it performs mitigation in an end-to-end framework which requires only a shadowed image for inference. We apply this process to the Landsat 8 OLI SPARCS validation data set and demonstrate plausible results.

Couwenhoven, Scott↗
DOE OSTI2020

Understanding and Mitigating the Contamination of Intrinsic poly-Si Gaps in Passivated IBC Solar Cells

We investigate factors that are critical for the performance of interdigitated back contact (IBC) solar cells based on polycrystalline silicon (poly-Si) passivated contacts. During patterning of doped lines using direct plasma deposition through a shadow mask, we show that the intrinsic poly-Si gap becomes contaminated with dopants, leading to shunting. Possible contamination mechanisms during masked deposition and high- temperature annealing are tested. Strategies developed to mitigate the contamination, such as reactive ion etching after deposition and amorphous to poly-Si crystallization in oxygen, will lead to improved IBC fabrication methods.

14 SOLAR ENERGY↗
DOE OSTI2025

Crack Identification and Characterization in Deformed Nb3Sn Rutherford Cable Stacks Using Machine Learning

An investigation of instance segmentation of cracks in Nb3Sn 4-stack 40-strand Rutherford cables using machine learning is presented. Three samples were uniaxially and biaxially loaded before metallographic inspections were performed. The Mask R-CNN model was used in the Detectron2 framework with pre-trained weights but fine-tuned to detect and segment cracks. The model detected cracks with bounding box and mask average precisions (AP) of 42.8 and 27.9, respectively, and was used for instance segmentation of all cracks in the three samples. More cracks were found in the sample pre-loaded along the z-axis (i.e., along the cable length). Pre-loading along the x-axis (i.e., on the cables edges) reduced the number of cracks and changed the crack orientation distribution, away from being highly aligned with the y-axis (i.e., normal to the cables broad faces), i.e., the direction with the highest applied load. Fine-tuning of the Segment Anything Model (SAM) was also studied but performed poorly without human-provided prompts. However, the zero-shot capability of SAM showed high promises to accelerate the image annotation process for applications beyond this study.

Croteau, Jean-Francois↗
DOE OSTI2019

Stretchable Mould Interconnect Optimization: Peeling Automation and Carrierless Techniques

The primary bottleneck of the stretchable mold interconnect (SMI) technology is its reliance on carrier boards. These are necessary to handle the meandered circuit during production and to ensure dimensional stability of the flexible circuit board before encapsulation. However, for all the problems it solves, it also introduces a new major problem by requiring a peeling step – which is difficult to automate. This manuscript aims to present some of the work that went into eliminating this problem, discussing both unsuccessful and functioning methods to tackle this conundrum and some of the experimental work that went into verifying these techniques. First, alterations to the design to simplify peeling are considered, followed by adhesivebased peeling processes and mechanical pin-based systems. Next, masking and structuring of the carrier board adhesive are considered. Finally, two carrierless methods which circumvent the problems are discussed, a two-step process – which cuts temporary support structures after partial encapsulation – and a technique whereby the frame is designed to fail in a controlled manner during the first use of the circuit, creating a carrierless process feasible for high-volume production.

Plovie, Bart↗
DOE OSTI2015

Software resilience and the effectiveness of software mitigation in microcontrollers

Commercially available microprocessors could be useful to the space community for noncritical computations. There are many possible components that are smaller, lower-power, and less expensive than traditional radiation-hardened microprocessors. Many commercial microprocessors have issues with single-event effects (SEEs), such as single-event upsets (SEUs) and single-event transients (SETs), that can cause the microprocessor to calculate an incorrect result or crash. In this paper we present the Trikaya technique for masking SEUs and SETs through software mitigation techniques. Furthermore, test results show that this technique can be very effective at masking errors, making it possible to fly these microprocessors for a variety of missions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Robust Duplication with Comparison Methods in Microcontrollers

Commercial microprocessors could be useful computational platforms in space systems, as long as the risk is bound. Many spacecraft are computationally constrained because all of the computation is done on a single radiation-hardened microprocessor. It is possible that a commercial microprocessor could be used for configuration, monitoring and background tasks that are not mission critical. Most commercial microprocessors are affected by radiation, including single-event effects (SEEs) that could be destructive to the component or corrupt the data. Part screening can help designers avoid components with destructive failure modes, and mitigation can suppress data corruption. We have been experimenting with a method for masking radiation-induced faults through the software executing on the microprocessor. While triple-modular redundancy (TMR) techniques are very effective at masking faults in software, the increased amount of execution time to complete the computation is not desirable. Here in this article we present a technique for combining duplication with compare (DWC) with TMR that decreases observable errors by as much as 145 times with only a 2.35 time decrease in performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values

Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, the observed data may contain negative values due to noise even when the true underlying physical signal is strictly positive. Prior NMF work has not treated negative data in a statistically consistent manner, which becomes problematic for low signal-to-noise data with many negative values. In this paper we present two algorithms, Shift-NMF and Nearly-NMF, that can handle both the noisiness of the input data and also any introduced negativity. Both of these algorithms use the negative data space without clipping or masking and recover non-negative signals without any introduced positive offset that occurs when clipping or masking negative data. We demonstrate this numerically on both simple and more realistic examples, and prove that both algorithms have monotonically decreasing update rules.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Lidar Cloud Detection with Fully Convolutional Networks

In this contribution, we present a novel approach for segmenting laser radar (lidar) imagery into geometric time- height cloud locations with a fully convolutional network (FCN). We describe a semi-supervised learning method to train the FCN by: pre-training the classification layers of the FCN with image-level annotations, pre-training the en- tire FCN with the cloud locations of the MPLCMASK cloud mask algorithm, and fully supervised learning with hand-labeled cloud locations. We show the model achieves higher levels of cloud identification compared to the cloud mask algorithm implementation.

Cromwell, Erol LD↗
DOE OSTI2022

ASDFL: An adaptive super‐pixel discriminative feature‐selective learning for vehicle matching

Abstract There are a large number of cameras in modern transportation system that capture numerous vehicle images continuously. Therefore, automatic analysis of these vehicle images is helpful for traffic flow management, criminal investigations and vehicle inspections. Vehicle matching, which aims to determine whether two input images depict an identical vehicle, is one of the core tasks in vehicle analysis. Recent relevant studies have focused on local feature extraction instead of global extraction, since local details can provide crucial cues to distinguish between cars. However, these methods do not select local features; that is, they do not assign weights to local features. Therefore, in this research, we systematically study the vehicle matching task, and present a novel annotation‐free local‐based deep learning method called Adaptive super‐pixel discriminative feature‐selective learning (ASDFL) to address this issue. In ASDFL, vehicle images are segmented into clusters of super‐pixels of similar size by considering the location and colour similarities of pixels without using any component‐level annotation. These super‐pixels are deemed to be the virtual components of vehicles. Moreover, a convolutional neural network is used to extract the deep features of these virtual components. Thereafter, an instance‐specific mask generation module driven by the extracted global features is enhanced to produce a mask to select the most distinctive virtual components of each vehicle image pair in the feature space. Finally, the vehicle matching task is accomplished by classifying the selected virtual component features of each imaged vehicle pair. Extensive experiments on two popular vehicle identification benchmarks demonstrate that our method is 1.57% and 0.8% more accurate than the previous baselines in a vehicle matching task on the VeRi and VehicleID datasets, respectively, which demonstrates the effectiveness of our method.

Qin, Rong↗
DOE OSTI2025

The development and application of the stirred‐reactor coupon analysis (SRCA) test method

A new technique, termed the stirred‐reactor coupon analysis (SRCA) method, has been developed to measure the rate of glass dissolution in forward‐rate conditions. Monolithic glass coupons are partially masked with an inert material before placement in a large volume of well‐mixed solution with known chemistry and temperature for a predetermined duration. After the test, the mask is removed, and the difference in step height between the protected area and the exposed corroded portions of the sample coupon is measured to determine the extent of glass dissolution. The step height is converted to a rate measurement using the test duration and glass density. Test parameters such as sample surface preparation and test duration were evaluated to determine their effects on the measured rates. Additionally, results from an interlaboratory study (ILS) consisting of 12 laboratories from 11 different institutions are presented, where each laboratory performed 12 independent tests. When removing experimental outlier data, the 95% reproducibility limits for the SRCA method has no statistical difference with previously published standardized test methods used to determine the forward rate of glass dissolution. Overall, this paper describes steps necessary to perform the test method and provides the statistical calculations to evaluate test accuracy.

chemical durability↗
DOE OSTI2014

Comparative study of GaN mesa etch characteristics in Cl{sub 2} based inductively coupled plasma with Ar and BCl{sub 3} as additive gases

GaN thin film etching is investigated and compared for mesa formation in inductively coupled plasma (ICP) of Cl{sub 2} with Ar and BCl{sub 3} gas additives using photoresist mask. Etch characteristics are studied as a function of ICP process parameters, viz., ICP power, radio frequency (RF) power, and chamber pressure at fixed total flow rate. The etch rate at each ICP/RF power is 0.1–0.2 μm/min higher for Cl{sub 2}/Ar mixture mainly due to higher Cl dissociation efficiency of Ar additive that readily provides Cl ion/radical for reaction in comparison to Cl{sub 2}/BCl{sub 3} mixture. Cl{sub 2}/Ar mixture also leads to better photoresist mask selectivity. The etch-induced roughness is investigated using atomic force microscopy. Cl{sub 2}/Ar etching has resulted in lower root-mean-square roughness of GaN etched surface in comparison to Cl{sub 2}/BCl{sub 3} etching due to increased Ar ion energy and flux with ICP/RF power that enhances the sputter removal of etch product. The GaN surface damage after etching is also evaluated using room temperature photoluminescence and found to be increasing with ICP/RF power for both the etch chemistries with higher degree of damage in Cl{sub 2}/BCl{sub 3} etching under same condition.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Investigation of electrochemical etch differences in AlGaAs heterostructures using Cl{sub 2} ion beam assisted etching

A deeply etched, anisotropic 45° and 90° mirror technology is developed for Al{sub x}Ga{sub 1−x}As heterostructures using a Cl{sub 2} ion beam assisted etching system. When etching vertically, using a conductive low-erosion Ni mask, electrochemical etch differences between layers with various Al mole fractions caused nonuniform sidewall profiles not seen in semi-insulating GaAs test samples. These variations, based on alloy composition, were found to be negligible when etching at a 45°. A Si{sub 3}N{sub 4}-Ni etch mask is designed in order to electrically isolate charge buildup caused by the incoming Ar{sup +} ion beam to the Ni layer, preventing conduction to the underlying epitaxial layers. This modification produced smoothly etched facets, up to 8 μm in depth, enabling fabrication of substrate–surface-emitting slab-coupled optical waveguide lasers and other optoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Application of cyclic fluorocarbon/argon discharges to device patterning

With increasing demands on device patterning to achieve smaller critical dimensions and pitches for the 5 nm node and beyond, the need for atomic layer etching (ALE) is steadily increasing. In this work, a cyclic fluorocarbon/Ar plasma is successfully used for ALE patterning in a manufacturing scale reactor. Self-limited etching of silicon oxide is observed. The impact of various process parameters on the etch performance is established. The substrate temperature has been shown to play an especially significant role, with lower temperatures leading to higher selectivity and lower etch rates, but worse pattern fidelity. The cyclic ALE approach established with this work is shown to have great potential for small scale device patterning, showing self-limited etching, improved uniformity and resist mask performance.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Review Article: Progress in fabrication of transition metal dichalcogenides heterostructure systems

Transition metal dichalcogenide (TMDC) semiconductors have attracted significant attention because of their rich electronic/photonic properties and importance for fundamental research and novel device applications. These materials provide a unique opportunity to build up high quality and atomically sharp heterostructures because of the nature of weak van der Waals interlayer interactions. The variable electronic properties of TMDCs (e.g., band gap and their alignment) provide a platform for the design of novel electronic and optoelectronic devices. The integration of TMDC heterostructures into the semiconductor industry is presently hindered by limited options in reliable production methods. Many exciting properties and device architectures which have been studied to date are, in large, based on the exfoliation methods of bulk TMDC crystals. These methods are generally more difficult to consider for large scale integration processes, and hence, continued developments of different fabrication strategies are essential for further advancements in this area. In this review, the authors highlight the recent progress in the fabrication of TMDC heterostructures. The authors will review several methods most commonly used to date for controllable heterostructure formation. One of the focuses will be on TMDC heterostructures fabricated by thermal chemical vapor deposition methods which allow for the control over the resulting materials, individual layers and heterostructures. Another focus would be on the techniques for selective growth of TMDCs. The authors will discuss conventional and unconventional fabrication methods and their advantages and drawbacks and will provide some guidance for future improvements. Mask-assisted and mask-free methods will be presented, which include traditional lithographic techniques (photo- or e-beam lithography) and some unconventional methods such as the focus ion beam and the recently developed direct-write patterning approach, which are shown to be promising for the fabrication of quality TMDC heterostructures.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗