Dr. Lijian Sun Profile

Dr. Lijian Sun

at Shanghai Jiao Tong University

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Proceedings Article | 27 September 2016 Paper

Gaussian process based intelligent sampling for measuring nano-structure surfaces

L. J. Sun, M. J. Ren, Y. H. Yin

Proceedings Volume 9684, 96840L (2016) https://doi.org/10.1117/12.2241769

KEYWORDS: Scanning probe microscopy, Composites, Reverse modeling, Data modeling, Tolerancing, Nanotechnology, Inspection, Statistical modeling, Process modeling, Manufacturing

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Nanotechnology is the science and engineering that manipulate matters at nano scale, which can be used to create many new materials and devices with a vast range of applications. As the nanotech product increasingly enters the commercial marketplace, nanometrology becomes a stringent and enabling technology for the manipulation and the quality control of the nanotechnology. However, many measuring instruments, for instance scanning probe microscopy, are limited to relatively small area of hundreds of micrometers with very low efficiency. Therefore some intelligent sampling strategies should be required to improve the scanning efficiency for measuring large area. This paper presents a Gaussian process based intelligent sampling method to address this problem. The method makes use of Gaussian process based Bayesian regression as a mathematical foundation to represent the surface geometry, and the posterior estimation of Gaussian process is computed by combining the prior probability distribution with the maximum likelihood function. Then each sampling point is adaptively selected by determining the position which is the most likely outside of the required tolerance zone among the candidates and then inserted to update the model iteratively. Both simulationson the nominal surface and manufactured surface have been conducted on nano-structure surfaces to verify the validity of the proposed method. The results imply that the proposed method significantly improves the measurement efficiency in measuring large area structured surfaces.

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