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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Patterning of self-assembled monolayers by phase-shifting mask and its applications in large-scale assembly of nanowires

A nonselective micropatterning method of self-assembled monolayers (SAMs) based on laser and phase-shifting mask (PSM) is demonstrated. Laser beam is spatially modulated by a PSM, and periodic SAM patterns are generated sequentially through thermal desorption. Patterned wettability is achieved with alternating hydrophilic/hydrophobic stripes on octadecyltrichlorosilane monolayers. The substrate is then used to assemble CdS semiconductor nanowires (NWs) from a solution, obtaining well-aligned NWs in one step. Our results show valuably the application potential of this technique in engineering SAMs for integration of functional devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Nanoselective area growth of GaN by metalorganic vapor phase epitaxy on 4H-SiC using epitaxial graphene as a mask

We report the growth of high-quality triangular GaN nanomesas, 30-nm thick, on the C-face of 4H-SiC using nanoselective area growth (NSAG) with patterned epitaxial graphene grown on SiC as an embedded mask. NSAG alleviates the problems of defects in heteroepitaxy, and the high mobility graphene film could readily provide the back low-dissipative electrode in GaN-based optoelectronic devices. A 5–8 graphene-layer film is first grown on the C-face of 4H-SiC by confinement-controlled sublimation of silicon carbide. Graphene is then patterned and arrays of 75-nm-wide openings are etched in graphene revealing the SiC substrate. A 30-nm-thick GaN is subsequently grown by metal organic vapor phase epitaxy. GaN nanomesas grow epitaxially with perfect selectivity on SiC, in the openings patterned through graphene. The up-or-down orientation of the mesas on SiC, their triangular faceting, and cross-sectional scanning transmission electron microscopy show that they are biphasic. The core is a zinc blende monocrystal surrounded with single-crystal wurtzite. The GaN crystalline nanomesas have no threading dislocations or V-pits. This NSAG process potentially leads to integration of high-quality III-nitrides on the wafer scalable epitaxial graphene/silicon carbide platform.

36 MATERIALS SCIENCE↗
DOE OSTI2016

High-throughput shadow mask printing of passive electrical components on paper by supersonic cluster beam deposition

We report the rapid prototyping of passive electrical components (resistors and capacitors) on plain paper by an additive and parallel technology consisting of supersonic cluster beam deposition (SCBD) coupled with shadow mask printing. Cluster-assembled films have a growth mechanism substantially different from that of atom-assembled ones providing the possibility of a fine tuning of their electrical conduction properties around the percolative conduction threshold. Exploiting the precise control on cluster beam intensity and shape typical of SCBD, we produced, in a one-step process, batches of resistors with resistance values spanning a range of two orders of magnitude. Parallel plate capacitors with paper as the dielectric medium were also produced with capacitance in the range of tens of picofarads. Compared to standard deposition technologies, SCBD allows for a very efficient use of raw materials and the rapid production of components with different shape and dimensions while controlling independently the electrical characteristics. Discrete electrical components produced by SCBD are very robust against deformation and bending, and they can be easily assembled to build circuits with desired characteristics. The availability of large batches of these components enables the rapid and cheap prototyping and integration of electrical components on paper as building blocks of more complex systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

X-Ray reduction imaging of inclined reflective masks at critical angles

We have proposed and simulated optical schemes for producing reduced images by X-ray lasers or harmonic generators at a wavelength of ∼14 nm. The mask in this case is placed at a small angle to the optical axis, corresponding to the angle of total external reflection of the material. We have determined the optimal position of the detector (resist) and the corresponding spatial resolution. The results can be used to solve problems in nanotechnology and nanostructuring of surfaces. (x-ray optics)

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

An adaptive masking method for beam halo measurement by a digital micro mirror device

We report that in high intensity particle beams, irregularities in cathode emission, scattering effects, dark current and space charge forces can lead to a common phenomenon termed beam halo with associated emittance growth, beam loss and overall beam quality degradation. It is of fundamental interest and practical importance to precisely monitor such beam halo, which could offer valuable insights into the experimental strategy for the suppression of this undesired feature. A Digital Micro mirror Device (DMD) is a computer-controlled micro-electrical-mechanical system that contains a compact array of physically independent reflective mirrors. Using a standard DMD, we come up with an adaptive masking method for halo measurement, which demonstrates superior robustness and efficiency comparing to the conventional approaches as confirmed by simulation and experimental results. Our development includes an integrated package of hardware, software and a graphic user interface (GUI) that is robust and portable and can be applied as a beam halo diagnostic for electron guns or radiating charged particle beams in general.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Is tokenization needed for masked particle modeling?

In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics. In MPM, a model is trained to recover the missing elements of a set, a learning objective that requires no labels and can be applied directly to experimental data. We achieve significant performance improvements over previous work on MPM by addressing inefficiencies in the implementation and incorporating a more powerful decoder. We compare several pre-training tasks and introduce new reconstruction methods that utilize conditional generative models without data tokenization or discretization. We show that these new methods outperform the tokenized learning objective from the original MPM on a new test bed for foundation models for jets, which includes using a wide variety of downstream tasks relevant to jet physics, such as classification, secondary vertex finding, and track identification.

conditional generative models↗
DOE OSTI2026

Particle trajectory representation learning with masked point modeling

Liquid argon time projection chambers (LArTPCs) offer millimeter-scale 3D images of particle trajectories, enabling precision studies of neutrino oscillation, detection of supernova and solar neutrinos, searches for exotic dark matter, and proton decay. Current approaches utilize supervised machine learning models, requiring extensive simulations of particle physics and detector response that can introduce bias. Self-supervised learning (SSL), a machine learning approach that learns useful representations of unlabeled data from the data itself, has significantly advanced how large datasets are utilized for representation learning; however, its potential for applications to sensory data in high precision particle physics experiments remains largely unexplored. We introduce the Point-based liquid argon masked autoencoder (PoLAr-MAE), a self-supervised framework that learns physically meaningful representations directly from unlabeled LArTPC images. PoLAr-MAE achieves remarkable data efficiency for a point-level segmentation task, outperforming fully supervised methods in low data regimes. Linear classifiers on model outputs demonstrate robust performance across multiple downstream tasks. Our results position sensor-level SSL as a practical foundation model strategy for LArTPCs.

Young, Samuel [Stanford Univ., CA (United States)]↗
DOE OSTI2021

Galaxy Zoo: 3D – crowdsourced bar, spiral, and foreground star masks for MaNGA target galaxies

ABSTRACT The challenge of consistent identification of internal structure in galaxies – in particular disc galaxy components like spiral arms, bars, and bulges – has hindered our ability to study the physical impact of such structure across large samples. In this paper we present Galaxy Zoo: 3D (GZ:3D) a crowdsourcing project built on the Zooniverse platform that we used to create spatial pixel (spaxel) maps that identify galaxy centres, foreground stars, galactic bars, and spiral arms for 29 831 galaxies that were potential targets of the MaNGA survey (Mapping Nearby Galaxies at Apache Point Observatory, part of the fourth phase of the Sloan Digital Sky Surveys or SDSS-IV), including nearly all of the 10 010 galaxies ultimately observed. Our crowdsourced visual identification of asymmetric internal structures provides valuable insight on the evolutionary role of non-axisymmetric processes that is otherwise lost when MaNGA data cubes are azimuthally averaged. We present the publicly available GZ:3D catalogue alongside validation tests and example use cases. These data may in the future provide a useful training set for automated identification of spiral arm features. As an illustration, we use the spiral masks in a sample of 825 galaxies to measure the enhancement of star formation spatially linked to spiral arms, which we measure to be a factor of three over the background disc, and how this enhancement increases with radius.

Masters, Karen L. (ORCID:0000000308469578)↗
DOE OSTI2022

AbBERT: Learning Antibody Humanness via Masked Language Modeling

Understanding the degree of humanness of antibody sequences is critical to the therapeutic antibody development process to reduce the risk of failure modes like immunogenicity or poor manufacturability. We introduce AbBERT, a transformer-based language model trained on up to 20 million unpaired heavy/light chain sequences from the Observed Antibody Space database. We first validate AbBERT using a novel “multi-mask” scoring procedure to demonstrate high accuracy in predicting complementary determining regions—including the challenging hypervariable H3 region. We then demonstrate several uses of AbBERT at various points along the antibody design process. AbBERT enhances in silico antibody optimization via deep reinforcement learning by utilizing its learned embeddings as additional observations during optimization. Within a larger computational antibody design platform, AbBERT has been successfully applied as an additional design objective, where it displays strong correlations with computational tools predicting antibody structural stability. Finally, mutant antibody sequences that have been scored as unfavorable by AbBERT have shown corresponding low yields when expressed in cells. These use cases demonstrate the power of language modeling within computational antibody design.

Bioinformatics↗
DOE OSTI2017

Computer-assisted area detector masking

Area detectors have become the predominant type of detector for the rapid acquisition of X-ray diffraction, small-angle scattering and total scattering. These detectors record the scattering for a large area, giving each shot good statistical significance to the resulting scattered intensity I ( Q ) pattern. However, many of these detectors have pixel level defects, which cause error in the resulting one-dimensional patterns. In this work, new software to automatically find and mask these dead pixels and other defects is presented. This algorithm is benchmarked with both ideal simulated and experimental datasets.

Wright, Christopher J.↗
DOE OSTI2024

Transformer Masked Autoencoders for RF Device Fingerprinting

Machine learning methods for RF device fingerprinting typically rely on CNN-based models. Transformer-based models have outperformed CNNs for modulation classification tasks, but there are few implementations for device fingerprinting. We train a transformer for device fingerprinting with the largest device count to date and explore several variations of the architecture. Additionally, we demonstrate that pre-training an RF transformer as a Masked Autoencoder improves classification accuracy, as has been observed for CNN fingerprinting models and vision transformers.

artificial intelligence↗
DOE OSTI2023

Safe Exploration Reinforcement Learning for Load Restoration using Invalid Action Masking

This paper addresses the load restoration problem after a power outage event. Our primary proposed methodology uses a multi-agent reinforcement learning method to make the optimal sequential decisions on picking up critical loads. Typically, a negative reward is provided to discourage the agents from selecting decisions that violate physical constraints during the restoration process. However, the main disadvantage of this approach is its difficulty in applying it to large-scale systems due to the curse of dimensionality. This paper introduces the invalid action masking technique to overcome this limitation. The features of this technique include zero physical constraint violations, reduced training time, and stabilization of the explo- ration process. Simulation results are performed in IEEE 13-node and IEEE 123-node systems showing the better performance of the proposed algorithm in comparison to the conventional approaches both in terms of restored power and learning curve.

reinforcement learning, blackstart, artificial int↗
DOE OSTI2026

Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks

Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user’s view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.

Large-scale data↗
DOE OSTI2019

Temperature and oxygen concentration effects on anisotropy in chromium hard mask etching for nanoscale fabrication

Chromium and its oxides are valuable as functional materials and plasma-etching hard masks in micro- and nanofabrication. While the continuous decrease in feature sizes opens new opportunities for applications of patterned chromium, the demands placed on the patterning process require a new level of mechanistic understanding. In this work, we investigate plasma etching with chlorine/oxygen chemistry using temperature and oxygen concentration as a means to controllably pattern chromium at the nanometer level. Building on our previous studies on blanket film chromium etching, we look here at patterned trenches 15–100 nm wide in 20 and 10 nm thick chromium films. We vary oxygen concentrations from 1.5% to 81% at substrate temperatures of +20 and −50 °C, respectively. Feature and trench profiles are evaluated from high-resolution cross-sectional SEM images. The ternary chromium etch product shows very different profiles as oxygen, temperature, and feature size are varied. We find the highest anisotropy at −50 °C and 50% oxygen concentration. Etch results suggest that the redeposition of nonvolatile intermediate reaction compounds enhances anisotropy at low temperature, whereas re-emission of reactive species causes undercut at the higher etching temperature.

Staaks, Daniel (ORCID:0000000204825507)↗
DOE OSTI2016

Emulation of anamorphic imaging on the SHARP extreme ultraviolet mask microscope

The SHARP high-numerical aperture actinic reticle review project is a synchrotron-based, extreme ultraviolet (EUV) microscope dedicated to photomask research. SHARP emulates the illumination and imaging conditions of current EUV lithography scanners and those several generations into the future. An anamorphic imaging optic with increased mask-side numerical aperture (NA) in the horizontal and increased demagnification in the vertical direction has been proposed in this paper to overcome limitations of current multilayer coatings and extend EUV lithography beyond 0.33 NA. Zoneplate lenses with an anamorphic 4×/8× NA of 0.55 are fabricated and installed in the SHARP microscope to emulate anamorphic imaging. SHARP’s Fourier synthesis illuminator with a range of angles exceeding the collected solid angle of the newly designed elliptical zoneplates can produce arbitrary angular source spectra matched to anamorphic imaging. A target with anamorphic dense features down to 50-nm critical dimension is fabricated using 40 nm of nickel as the absorber. In a demonstration experiment, anamorphic imaging at 0.55 4×/8× NA and 6 deg central ray angle (CRA) is compared with conventional imaging at 0.5 4× NA and 8 deg CRA. A significant contrast loss in horizontal features is observed in the conventional images. Finally, the anamorphic images show the same image quality in the horizontal and vertical directions.

36 MATERIALS SCIENCE↗
DOE OSTI2018

EUV photolithography mask inspection using Fourier ptychography

Fourier ptychography is a computational imaging techniques that combines various full-field coherent images acquired under varied illumination angles and combined to yield a angular spectrum with a large synthetic numerical aperture and non-interferometric phase information. We present here the implementation of this technique in a full-field soft x-ray microscope designed to emulate modern EUV lithography tools imaging conditions, and we show that this technique can be used for the study of EUV photomasks. The technique allows us to quantitatively characterize phase defects (predominant in EUV lithography), to study new mask designs made of phase structures, to study sub-resolution assist features and extend the resolution of the microscope down to 26-nm, correspond to the N1 technology node.

Wojdyla, Antoine↗
DOE OSTI2022

Xanthohumol microbiome and signature in adults with Crohn’s disease (the XMaS trial): a protocol for a phase II triple-masked, placebo-controlled clinical trial

Xanthohumol (XN), a bioactive flavonoid from Humulus lupulus with anti-inflammatory properties, has potential benefits for patients with Crohn’s disease (CD), a type of inflammatory bowel disease. We recently completed and published results of a placebo-controlled phase I clinical trial demonstrating the safety and tolerability of 24 mg XN daily for 8 weeks. The present study aims to evaluate the safety and tolerability of the same dose of XN adults with clinically active CD in a placebo-controlled phase II clinical trial. Additional aims will assess the impact of XN on inflammatory biomarkers, platelet function, CD clinical activity, and stool microbial composition. The metabolism of XN will also be evaluated. This article provides a model protocol for consideration in investigations of XN or other natural products in disease states. A triple-masked, randomized, placebo-controlled trial will be conducted in adults with clinically active CD. Participants (n ≤ 32) will be randomized to either 24 mg encapsulated XN per day or placebo and followed for 8 weeks. Throughout the trial, participants will be queried for adverse events. Biomarkers of clinical safety, blood and stool markers of inflammation, platelet function, Crohn’s Disease Activity Index score, stool microbial composition, and XN metabolite profiles in blood, urine, and stool will be assessed every 2 weeks. We describe the protocol for a phase II clinical trial that evaluates the safety and tolerability of XN in adults with active CD, as well as evaluate metabolism and mechanisms that are relevant to CD and other diseases with underlying inflammation and/or gut permeability. The effects of XN on inflammatory biomarkers, platelet function, the microbiota, and multi-omics biomarkers measured in this phase II trial of adults with CD will be compared to the effects of XN in healthy adults in our previous phase I trial. The results of the study will advance the evidence guiding the use of XN in patients with CD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Extreme ultraviolet mask roughness effects in high numerical aperture lithography

Given the reflective nature of extreme ultraviolet lithography and its extremely short operational wavelength, roughness of the optical surfaces is of significant concern. In particular, roughness in the mask multilayer leads to image plane speckle and ultimately patterned line-edge or line-width variability in the imaging process. Here we consider the implications of this effect for future high numerical aperture (NA) systems that are assumed to require anamorphic magnification projection optics. The results show significant anisotropic behavior at high NA as well as a substantial increase in relative patterned line variability in the shadowed direction when comparing 0.55 NA to 0.33 NA, despite the assumption of an anamorphic magnification system. In conclusion, the shadowed-direction patterned line variability is 2× larger than for unshadowed lines, and the majority of the increase in variability occurs in the low frequency regime.

47 OTHER INSTRUMENTATION↗