Mr. Harry S. Gallarda Profile

Harry S. Gallarda

Vice President of Algorithms

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Proceedings Article | 1 July 1991 Paper

Computational model of the imaging process in scanning-x microscopy

Harry Gallarda, Ramesh Jain

Proceedings Volume 1464, (1991) https://doi.org/10.1117/12.44458

KEYWORDS: Image processing, Scanning tunneling microscopy, Mathematical modeling, Composites, Inspection, Scanning electron microscopy, Convolution, Reconstruction algorithms, Spatial resolution, Integrated circuits

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STM (Scanning Tunneling Microscopy) and its variants, collectively called SXM (not including electron microscopy), are being increasingly used in electronics research and industry to study and inspect semiconductor surfaces. While it is possible to obtain quite high depth resolutions, lateral spatial resolution is chiefly determined by probe size and shape which is typically much larger than the depth resolution. Thus, an SXM image is not a reflection of the true surface shape but rather a 'convolution' of the surface and probe shapes. This paper reviews the theoretical and experimental work done in reconstructing surface shape from SXM images. The authors present a computational model of the SXM imaging process that encompasses previous models and show that the imaging process (convolution) is essentially a nonlinear operation and can be approximated mathematically by a morphological dilation between the surface and probe shape. The authors address the problem of inverting this process to estimate the true surface shape. A general method is developed by which a surface can be reconstructed from a composition of SXM images produced by different scanning probes. A multi-resolution version of this composite method is then described using a set of multiscaled probes that can recursively and efficiently reconstruct the entire surface as though it had been scanned entirely by the smallest probe. Some other useful and interesting results of our SXM imaging model are presented. The authors conclude by discussing the theoretical and practical importance of their computational model of SXM imaging process and directions for future work.

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