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Stanley S. C. Chim

Researcher at Stanford University

Publications -  11
Citations -  782

Stanley S. C. Chim is an academic researcher from Stanford University. The author has contributed to research in topics: Microscope & Interference microscopy. The author has an hindex of 5, co-authored 11 publications receiving 765 citations.

Papers
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Journal ArticleDOI

Mirau correlation microscope.

TL;DR: A correlation microscope based on the Mirau interferometer configuration using a thin silicon nitride film beam splitter is constructed, which predicts accurately both the transverse resolution at a sharp edge and the range resolution for a perfect plane reflector.
Journal ArticleDOI

Three-dimensional image realization in interference microscopy.

TL;DR: An efficient algorithm based on the Hilbert transform for reconstructing cross-sectional or three-dimensional images from the input images acquired by an interference microscope is described and can be easily implemented with a low-cost frame grabber.
Patent

Method of providing images of surfaces with a correlation microscope by transforming interference signals

TL;DR: In this paper, a method of processing the interference signals form a correlation microscope to provide images of a surface being examined is presented. But the method is not suitable for the case where the interference signal for a plurality of x,y locations is transformed to provide an intensity envelope of the signals along the z axis for each x, y location.
Journal ArticleDOI

Phase measurements using the Mirau correlation microscope

TL;DR: A new algorithm for extracting amplitude and phase information from the data collected by the Mirau correlation microscope is presented and experimental results with metal gratings show good agreement with Fourier theory.
Patent

Method and apparatus of measuring line structures with an optical microscope by data clustering and classification

TL;DR: In this paper, a method of processing images obtained with a microscope along a line on an object, in which the object, having trenches or lines, is moved in the z direction to form a cloud plot, signals in the x direction are scanned to create a linescan vector; the linescan vectors are clustered and classified; a centroid is defined; a similarity curve is created from the lines can vectors and the centroids; and the distance between adjacent points on the similarity curve are measured to provide a measure of line or trench width.