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Proceedings Paper

Future directions of nanometrology and nanomanufacturing
Author(s): Kevin W. Lyons
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Paper Abstract

Nanomanufacturing and nanometrology are key enablers for fulfilling the promise of nanotechnology. Nanomanufacturing (NM) capitalizes on the special material properties and processing capabilities at the nanoscale, and promotes integration of nanostructures to functional micro devices and meso/macroscale architectures and systems, as well as the interfacing issues across dimensional scales. Nanometrology provides the means to measure and characterize process and product performance and covers an expanse of topics including instrumentation, measurement approaches for off-line and in-process production applications, and standards. To meet the needs of this emerging manufacturing community it is important that research on scale-up of nanotechnology for high rate production, reliability, robustness, yield, efficiency and cost issues for manufacturing products and services be pursued. To achieve this, new research directions must include a systems approach that encompasses nanoscale materials and structures, fabrication and integration processes, production equipment and characterization of instrumentation, theory/ modeling/ simulation and control tools, biomimetic design and integration of multi-scale functional systems, three dimensional nanoscale metrology, production-hardened metrology, and other areas driven by industrial applications. Impact is expected in energy systems, electronics, medical/health, transportation, pharmaceutical, chemicals and defense sectors.

Paper Details

Date Published: 9 April 2007
PDF: 7 pages
Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 65760L (9 April 2007); doi: 10.1117/12.725206
Show Author Affiliations
Kevin W. Lyons, National Institute of Standards and Technology (United States)

Published in SPIE Proceedings Vol. 6576:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V
Harold H. Szu; Jack Agee, Editor(s)

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