Characterization of Surface Texture-Measuring Optical Microscopes using a Binary Pseudo-Random Array Standard

Published

October 3, 2022

Author(s)

Ulf Griesmann, Keiko Munechika, Thomas Renegar, Xiaoyu Zheng, Johannes Soons, Weilun Chao, Ian Lacey, Carlos Pina-Hernandez, Peter Takacs, Valeriy Yashchuk, Thomas Germer

Abstract

Accurate topography measurements of engineered surfaces over a wide range of spatial frequencies are required in many applications. The instrument transfer function (ITF) of the microscope used to characterize the surface topography must be taken into consideration to ensure that the height, or depth, of features with higher spatial frequency content is not underestimated. This applies especially when comparing surface topography measurements made by different types of microscopes. We discuss ITF measurements of a confocal microscope and an interferometric microscope using a binary pseudo-random array (BPRA) standard. BPRA standards are surfaces designed to have constant power spectral density (PSD) over the spatial frequency range of a microscope. The ITF of a microscope can thus be derived from a PSD measurement of a BPRA standard in a straight-forward manner. We further show how BPRA standards can be used as efficient diagnostic tools to characterize aspects of the imaging performance of topography-measuring microscopes.

Proceedings Title

Proceedings of SPIE 12223

Conference Dates

August 22-25, 2022

Conference Location

San Diego, CA, US

Conference Title

Interferometry XXI

Pub Type

Conferences

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Keywords

surface topography measurement, instrument transfer function, binary pseudo-random array standard

Citation

Griesmann, U. , Munechika, K. , Renegar, T. , Zheng, X. , Soons, J. , Chao, W. , Lacey, I. , Pina-Hernandez, C. , Takacs, P. , Yashchuk, V. and Germer, T. (2022), Characterization of Surface Texture-Measuring Optical Microscopes using a Binary Pseudo-Random Array Standard, Proceedings of SPIE 12223, San Diego, CA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935281 (Accessed November 12, 2025)