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6 December 2004 Sophisticated yield analysis through novel data clustering and visualization
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Abstract
Mask manufacturing becomes more complicated with each technology generation. The number of tools and processing steps is increasing while the feature geometries and substrate materials are becoming more difficult to process. At the same time, the market demands new product introductions at a faster rate than ever before. The only way to meet all of these challenges is through faster yield (critical dimensions, defects, registration) learning. However, the rate of technology development for manufacturing has far outpaced the development of yield learning solutions. Spreadsheet-based learning tools are severely limited in their ability to handle the complex hierarchical data, and often there is not enough data available for a meaningful statistical analysis. We have deployed a novel application that greatly enhances our ability to perform commonality studies, which are a key element of yield learning. This application is based on treemapping technology, which takes advantage of the human eye's ability to detect subtle changes. Here, data is represented graphically on a two-dimensional screen. However, additional dimensions are included on the same plot through the use of size, color, and hierarchical nesting. This has enabled us to be more sophisticated in our approaches to yield learning through visualizing multidimensional correlations. In addition to improving our ability to perform commonality studies, the tool has also been used for process stability analysis, hold and excursion analysis, and several other manufacturing and engineering applications.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael L. Jacobson, Jian Ma, Richard P. Rodrigues, and Robert L. O'Donnell "Sophisticated yield analysis through novel data clustering and visualization", Proc. SPIE 5567, 24th Annual BACUS Symposium on Photomask Technology, (6 December 2004); https://doi.org/10.1117/12.569346
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