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are seeking researchers to contribute to the development and application of advanced measurement and automation techniques for exploring processing-structure-property-performance (PSPP) relationships in
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301.975.3507 Description Recent developments in Artificial Intelligence (AI) have allowed machine learning models to solve certain complex problems in natural language processing and other areas at large scales
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that combine experience from traditional ceramic processing with recent breakthroughs in densification of ceramics, like cold sintering[2] or ultra-fast high-temperature sintering [3]. Our effort at NIST focuses
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the construction process and correlates to the durability and service life of the composites. The goal is to understand the interplay between structure-properties-performance within these systems
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. Furthermore, there is a need to understand how EUV resists may interface with other processing approaches such as block copolymer lithography. This project will utilize chemically sensitive scattering tools
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-acquisition circuitry, and signal-processing/pattern-recognition algorithms. The sensors must be tailored for the particular nature of a given chemical or biochemical measurement problem by optimizing and
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and novel data-processing tools need to be developed to embrace these new techniques and further elevate their capabilities. Instrumentation available for this research includes ion trap, Orbitrap, and
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Description Research opportunities are available to develop and advance measurement methods required for current and future semiconductor manufacturing processes. Areas of particular interest include
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. In this project, we are developing metrology needed for the synthesis, processing, and characterization of low-dimensional materials to enable reliable nanoscale device development and manufacturing
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the fundamental structure-property and processing-property relations that will enable these materials to provide the necessary performance in the wide spectrum of applications envisioned. For example, quantitative