Practical pattern recognition of high-magnification targets

Lewis A. Binns; Greg Reynolds; Timothy Rigden

doi:10.1117/12.599492

17 May 2005 Practical pattern recognition of high-magnification targets

Lewis A. Binns, Greg Reynolds, Timothy Rigden

Proceedings Volume 5755, Data Analysis and Modeling for Process Control II; (2005) https://doi.org/10.1117/12.599492
Event: Microlithography 2005, 2005, San Jose, California, United States

Abstract

Pattern matching has long been a cornerstone of industrial inspection. For example, in order to obtain high accuracy, modern overlay metrology tool optics are optimized to ensure symmetry around the central axis. To obtain best performance, the metrology target should be as close as possible to that axis, hence a pattern recognition stage is usually used to verify target position before measurement. However most of the work performed to date has concentrated on situations where the imaging process could be described by simple ray-tracing, where the image is formed by albedo difference between surfaces rather than interference. However, current semiconductor technology requires optical identification of targets less than 30 microns (i.e. about 50 wavelengths) across, and of order 1 wavelength deep, and this description is no longer valid; interference and focusing effects become dominant. In this paper we examine these effects, and their impact on a number of different techniques. We compare image-based and CAD-derived models in the training of the pattern recognition system; CAD-derived models are of particular interest due to their use in “imageless” recipe creation techniques. Our chief metrics are precision and reliability. We show that for both types of pattern matching approach, submicron precision and high reliability is achievable even in very challenging optical environments. We show that, while generally inferior to image based models, that models derived from design data are more robust to changes caused by process variation, namely changes in illumination, contrast and focus.

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Lewis A. Binns, Greg Reynolds, and Timothy Rigden "Practical pattern recognition of high-magnification targets", Proc. SPIE 5755, Data Analysis and Modeling for Process Control II, (17 May 2005); https://doi.org/10.1117/12.599492

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KEYWORDS

Pattern recognition

Semiconducting wafers

Target recognition

Image processing

Data modeling

Metals

Detection and tracking algorithms

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