4.3 Article

Three-dimensional fuzzy influence analysis of fitting algorithms on integrated chip topographic modeling

Journal

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
Volume 26, Issue 10, Pages 3177-3191

Publisher

KOREAN SOC MECHANICAL ENGINEERS
DOI: 10.1007/s12206-012-0832-6

Keywords

Surface-fitting algorithm; Performance influence; Integrated chip surface; Micro-topography; Three dimensional fuzzy analysis

Funding

  1. National Natural Science Foundation of China [51205073, 51275100]
  2. China Postdoctoral Science Foundation [2012M510197]
  3. Foundation for Distinguished Young Talents in Higher Education of Guangdong, China [LYM09110]
  4. Science and Technology Project of Guangzhou City [2012J4100053, 12C42011566]
  5. Research Project of Guangzhou Municipal University [10A068]
  6. Xinmiao Project of Guangzhou University [LZW2-2091]

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In inspecting the detailed performance results of surface precision modeling in different external parameter conditions, the integrated chip surfaces should be evaluated and assessed during topographic spatial modeling processes. The application of surface-fitting algorithms exerts a considerable influence on topographic mathematical features. The influence mechanisms caused by different surface-fitting algorithms on the integrated chip surface facilitate the quantitative analysis of different external parameter conditions. By extracting the coordinate information from the selected physical control points and using a set of precise spatial coordinate measuring apparatus, several typical surface-fitting algorithms are used for constructing micro-topographic models with the obtained point cloud. In computing for the newly proposed mathematical features on surface models, we construct the fuzzy evaluating data sequence and present a new three-dimensional fuzzy quantitative evaluating method. Through this method, the value variation tendencies of topographic features can be clearly quantified. The fuzzy influence discipline among different surface-fitting algorithms, topography spatial features, and the external science parameter conditions can be analyzed quantitatively and in detail. In addition, quantitative analysis can provide final conclusions on the inherent influence mechanism and internal mathematical relation in the performance results of different surface-fitting algorithms, topographic spatial features, and their scientific parameter conditions in the case of surface micro modeling. The performance inspection of surface precision modeling will be facilitated and optimized as a new research idea for micro-surface reconstruction that will be monitored in a modeling process.

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