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will be implemented and validated experimentally. Current applications of interest include, but are not limited to, semiconductor quantum dots and cold atom systems. [1] J. P. Zwolak et al. Auto-tuning
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functionalities, from crossbar-based machine learning to race-logic-based computing. Opportunities exist for experimental work in device fabrication and measurement using NIST’s state-of-the-art NanoFab and
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images. However, the current limitations of desktop computers in terms of memory, disk storage and computational power, and the lack of image processing algorithms for advanced parallel and distributed
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. Nature 502: 532-536, 2013 Orloff ND, et al: Noncontact conductivity and dielectric measurement for high throughput roll-to-roll nanomanufacturing. Science Reports 5: 17019, 2015 High-throughput
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materials. The primary focus of this work is on mechanical characterization, microstructural analysis, and finite element analysis (FEA) and artificial intelligence (AI)/machine learning (ML) modeling
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for the world. Likewise, U.S. manufacturing depends on its 500,000+ machine tools that make precision parts. However, a major problem with these machines is that their performance, which degrades over time due
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, economics, and all branches of science. Current concerns include the development and analysis of algorithms for the solution of problems of estimation, simulation and control of complex systems, and their
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chromatography/mass spectrometry (LC-MS) measurements using principal component analysis, partial least squares, genetic algorithms, and other multivariate statistics. Current projects have accumulated a
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requires increased patterning resolution to continue shrink device dimensions and follow Moore’s law. Block copolymer lithography provides a potential avenue for increasing the resolution of current
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local disorder as probed by X-ray Absorption Spectroscopy result in emergent material properties Machine-learning driven electrochemical deposition of coatings controlling surface properties