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Kumar, Dinesh

Publications and source records attributed to Kumar, Dinesh.

15 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Characterization of latent image of electron beam resist via critical-dimension resonant soft X-ray scattering

Metrology plays a crucial role in semiconductor manufacturing by providing accurate and precise measurement and characterization of critical parameters. With the development of high-resolution extreme ultraviolet lithography (EUVL) processes, critical dimensions are shrinking to sub-10 nm. Resist materials encounter the challenge of providing heightened sensitivity and a handle on exacerbating stochastic variations. A comprehensive understanding of the chemical profile of the latent image is pivotal for mitigating stochastic effects and optimizing pattern quality. However, the subtle differences in chemistry between the exposed and unexposed regions of the resists make it extremely challenging to characterize the latent images with sub-nanometer precision. Here, we develop the metrology with critical-dimension resonant soft X-ray scattering (CD-RSoXS) to probe the chemical profiles of latent images stored in resist after exposure. The combination of absorption spectroscopy and enhanced scattering contrast makes it possible to characterize the subtle structural and chemical variations in the latent image. Moreover, the results of the measurements are compared with the simulations with a finite element method–based Maxwell solver to extract a detailed profile of the latent and developed images. We demonstrate that the CD-RSoXS technique can provide valuable insights into the high spatial resolution and local chemical sensitivity simultaneously, which is crucial to understanding the resolution limits and stochastic effects in EUVL processes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

tomoCAM : fast model-based iterative reconstruction via GPU acceleration and non-uniform fast Fourier transforms

X-ray-based computed tomography is a well established technique for determining the three-dimensional structure of an object from its two-dimensional projections. In the past few decades, there have been significant advancements in the brightness and detector technology of tomography instruments at synchrotron sources. These advancements have led to the emergence of new observations and discoveries, with improved capabilities such as faster frame rates, larger fields of view, higher resolution and higher dimensionality. These advancements have enabled the material science community to expand the scope of tomographic measurements towards increasingly in situ and in operando measurements. In these new experiments, samples can be rapidly evolving, have complex geometries and restrictions on the field of view, limiting the number of projections that can be collected. In such cases, standard filtered back-projection often results in poor quality reconstructions. Iterative reconstruction algorithms, such as model-based iterative reconstructions (MBIR), have demonstrated considerable success in producing high-quality reconstructions under such restrictions, but typically require high-performance computing resources with hundreds of compute nodes to solve the problem in a reasonable time. Here, tomoCAM , is introduced, a new GPU-accelerated implementation of model-based iterative reconstruction that leverages non-uniform fast Fourier transforms to efficiently compute Radon and back-projection operators and asynchronous memory transfers to maximize the throughput to the GPU memory. The resulting code is significantly faster than traditional MBIR codes and delivers the reconstructive improvement offered by MBIR with affordable computing time and resources. tomoCAM has a Python front-end, allowing access from Jupyter -based frameworks, providing straightforward integration into existing workflows at synchrotron facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Large microwave inductance of granular boron-doped diamond superconducting films

Boron-doped diamond granular thin films are known to exhibit superconductivity with an optimal critical temperature of T c =7.2 K. Here, we report the measured in-plane complex surface impedance of boron-doped diamond films in the microwave frequency range using a resonant technique. Experimentally measured inductance values are in good agreement with estimates obtained from the normal state sheet resistance of the material. Additionally, the magnetic penetration depth temperature dependence is consistent with that of a fully gapped s-wave superconductor. Boron-doped diamond films should find application where high kinetic inductance is needed, such as microwave kinetic inductance detectors and quantum impedance devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Estimation of Line Cross Sections Using Critical-Dimension Grazing-Incidence Small-Angle X-Ray Scattering

Currently, the semiconductor industry is continuously pushing the limits of photolithography, with feature sizes now smaller than 10 nm. To ensure quality, it has become necessary to look beyond the conventional metrological techniques. X-ray scattering has emerged as a possible contender to determine the average shape of a line grating with subnanometer precision. Yet, to fulfill its promise, faster algorithms must also be developed to interpret and extract metrics from reciprocal-space scattering data. In this paper, we present a fast and accurate x-ray technique and analysis algorithm: critical-dimension grazing-incidence small-angle x-ray scattering (CD GISAXS). The CD GISAXS technique is used in grazing-incidence configuration with a continuous azimuthal rotation of the sample, and thus does not require high-energy x-rays to penetrate the wafer and greatly reduces the data-acquisition times, permitting analysis within the framework of the distorted-wave Born approximation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Reconstructing the three-dimensional latent image of extreme ultraviolet resists with resonant soft x-ray scattering

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). Extreme ultraviolet (EUV) lithography is one of the most promising printing techniques for high-volume semiconductor manufacturing at the 14-nm half-pitch device node and beyond. However, key challenges around EUV photoresist materials, such as the exposure-dose sensitivity or the line-width roughness, continue to impede its full adoption into industrial nanofab facilities. Metrology tools are required to address these challenges by helping to assess the impact of the EUV materials' properties and processing conditions along different steps of the nanofabrication process. We apply the resonant soft x-ray scattering (RSoXS) technique to gain insights into the structure of patterned EUV resists before the development step takes place. By using energies around the carbon K-edge to take advantage of small differences in chemistry, the electronic density contrast between the exposed and unexposed regions of the resists could be enhanced in order to image the patterns with subnanometer precision. Critical-dimension grazing-incidence small-angle x-ray scattering is then performed at energies where the contrast is maximized, enabling the reconstruction of the three-dimensional shape of the latent image. We demonstrate the potential of RSoXS to provide a high-resolution height-sensitive profile of patterned EUV resists, which will help in quantifying the evolution of critical features, such as the line-edge roughness, at a key step of the nanofabrication process.

Freychet, Guillaume↗
DOE OSTI2019

Convolutional neural networks for grazing incidence x-ray scattering patterns: thin film structure identification

Nano-structured thin films have a variety of applications from waveguides, gaseous sensors to piezoelectric devices. Grazing Incidence Small Angle x-ray Scattering images enable classification of such materials. One challenge is to determine structure information from scattering patterns alone. This paper highlights the design of multiple Convolutional Neural Networks (CNN) to classify nanoparticle orientation in a thin film by learning scattering patterns. The network was trained on several thin films with a success rate of 94%. We demonstrate CNN robustness under different noises as well as demonstrate the potential of our proposed approach as a strategy to decrease scattering pattern analysis time.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Using resonant soft x-ray scattering to image patterns on undeveloped resists

Copyright © 2018 SPIE. Extreme ultraviolet lithography is one of the most promising printing techniques for high volume semiconductor manufacturing at the 14 nm half-pitch device node and beyond. However, key challenges around EUV photoresist materials such as the exposure-dose sensitivity or the line-width roughness continue to impede the full adoption into industrial nanofab facilities. New metrology tools are required to address these challenges by helping to determine the impact of the EUV materials' properties and processing conditions on the roughness through the different step of the process. Here, we apply the resonant soft x-ray scattering (RSOXS) technique to gain insights into the structure of patterned EUV resists before the development step takes place. By using energies around the carbon absorption edge to take advantage of small differences in chemistry, the electronic density contrast between the exposed and unexposed regions of the resists could be enhanced in order to image the patterns with sub-nm precision. Critical-dimension grazing incidence small-angle X-ray scattering (CDGISAXS) was then performed at energies where the contrast was maximized, enabling the reconstruction of the 3D shape of the latent image. This demonstrates the potential of RSOXS to provide a high-resolution heightsensitive profile of patterned EUV resists, which will help to quantify the evolution of critical features, such as the line edge roughness, at each step of the nanofabrication process.

Pandolfi, Ronald J↗
DOE OSTI2018

Effect of Ag Addition on the Surface Topography and the Vibrational Dynamics of MgB{sub 2}

We fabricated MgB{sub 2} samples with Ag additions using in situ solid-state reaction via a single-step sintering to study the effect of Ag on the structural, vibration, and superconducting properties of MgB{sub 2} samples. Ag addition to MgB{sub 2} resulted in a significant improvement in J{sub c} although no appreciable effect was observed in the lattice parameters and the superconducting transition temperature T{sub c}. Dramatic increase in the grain size was observed with Ag addition and topographic measurements with atomic force microscopy revealed the formation of Ag–Mg nanoparticles 5–20 nm in size at 2 and 4 wt% Ag additions. The fact that these samples showed high J{sub c} values suggests that the nanoparticles formed as a result of Ag addition are responsible for enhanced flux pinning. Raman spectroscopy measurements showed that Ag additions also increased disorder in the system and thereby affected the line width of the Raman active E{sub 2g} mode.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Xi-cam : a versatile interface for data visualization and analysis

Xi-cam is an extensible platform for data management, analysis and visualization. Xi-cam aims to provide a flexible and extensible approach to synchrotron data treatment as a solution to rising demands for high-volume/high-throughput processing pipelines. The core of Xi-cam is an extensible plugin-based graphical user interface platform which provides users with an interactive interface to processing algorithms. Plugins are available for SAXS/WAXS/GISAXS/GIWAXS, tomography and NEXAFS data. With Xi-cam 's `advanced' mode, data processing steps are designed as a graph-based workflow, which can be executed live, locally or remotely. Remote execution utilizes high-performance computing or de-localized resources, allowing for the effective reduction of high-throughput data. Xi-cam 's plugin-based architecture targets cross-facility and cross-technique collaborative development, in support of multi-modal analysis. Xi-cam is open-source and cross-platform, and available for download on GitHub.

Pandolfi, Ronald J. (ORCID:0000000308248548)↗
DOE OSTI2018

Synthesis and luminescent properties of Tb3+ doped BaLa2ZnO5 nanoparticles

Highlights: • First combustion-synthesis report of BaLa{sub 2-x}ZnO{sub 5}:xTb{sup 3+} nanophosphors. • Lattice parameters and atomic positions have been refined. • Critical distance for non-radiative energy transfer has been calculated. • These nanophosphors are suitable candidate for solid state lighting. • Some blue to green color tunability has been found. - Abstract: Nanophosphor powders of composition BaLa{sub 2}ZnO{sub 5}:Tb{sup 3+} have been synthesized via using urea assisted solution combustion method using metal nitrates as the precursor materials. The structural, morphological and luminescent properties have been investigated by powder X-ray diffraction (XRD) and photoluminescence (PL) spectroscopy. The Rietveld analysis showed that these nanophosphors crystallize in the tetrahedral phase with the 14/mcm (140) space group. The crystallite size was determined by Scherer formula and was found to be 70 nm. The photoluminescent excitation of these nanocrystals at 278 nm yielded the characteristic emission peaks at 545 nm and 489 nm due to {sup 5}D{sub 4} → {sup 7}F{sub 5} (magnetic-dipole) and {sup 5}D{sub 4} → {sup 7}F{sub 6} (electric-dipole) transitions, respectively. The optimum activator concentration was found to be 3 mol% and the concentration quenching arising from the over-doping was attributed to the quadrupole–quadrupole interactions. The excellent luminescent profile of these nano-scaled particles suggests their auspicious applications in the solid-state lighting and display technology.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2017

Basic Energy Sciences Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Basic Energy Sciences, November 3-5, 2015, Rockville, Maryland

Computers have revolutionized every aspect of our lives. Yet in science, the most tantalizing applications of computing lie just beyond our reach. The current quest to build an exascale computer with one thousand times the capability of today’s fastest machines (and more than a million times that of a laptop) will take researchers over the next horizon. The field of materials, chemical reactions, and compounds is inherently complex. Imagine millions of new materials with new functionalities waiting to be discovered — while researchers also seek to extend those materials that are known to a dizzying number of new forms. We could translate massive amounts of data from high precision experiments into new understanding through data mining and analysis. We could have at our disposal the ability to predict the properties of these materials, to follow their transformations during reactions on an atom-by-atom basis, and to discover completely new chemical pathways or physical states of matter. Extending these predictions from the nanoscale to the mesoscale, from the ultrafast world of reactions to long-time simulations to predict the lifetime performance of materials, and to the discovery of new materials and processes will have a profound impact on energy technology. In addition, discovery of new materials is vital to move computing beyond Moore’s law. To realize this vision, more than hardware is needed. New algorithms to take advantage of the increase in computing power, new programming paradigms, and new ways of mining massive data sets are needed as well. This report summarizes the opportunities and the requisite computing ecosystem needed to realize the potential before us. In addition to pursuing new and more complete physical models and theoretical frameworks, this review found that the following broadly grouped areas relevant to the U.S. Department of Energy (DOE) Office of Advanced Scientific Computing Research (ASCR) would directly affect the Basic Energy Sciences (BES) mission need. Simulation, visualization, and data analysis are crucial for advances in energy science and technology. Revolutionary mathematical, software, and algorithm developments are required in all areas of BES science to take advantage of exascale computing architectures and to meet data analysis, management, and workflow needs. In partnership with ASCR, BES has an emerging and pressing need to develop new and disruptive capabilities in data science. More capable and larger high-performance computing (HPC) and data ecosystems are required to support priority research in BES. Continued success in BES research requires developing the next-generation workforce through education and training and by providing sustained career opportunities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Xi-CAM v1.2.3

Xi-CAM aims to provide a community driven platform for multimodal analysis in synchrotron science. The platform core provides a robust plugin infrastructure for extensibility, allowing continuing development to simply add further functionality. Current modules include tools for characterization with (GI)SAXS, Tomography, and XAS. This will continue to serve as a development base as algorithms for multimodal analysis develop. Seamless remote data access, visualization and analysis are key elements of Xi-CAM, and will become critical to synchrotron data infrastructure as expectations for future data volume and acquisition rates rise with continuously increasing throughputs. The highly interactive design elements of Xi-cam will similarly support a generation of users which depend on immediate data quality feedback during high-throughput or burst acquisition modes.

Pandolfi, Ronald↗