Clustering for data compression

Details Clustering for data compression Clustering for data compression

2005-02-15

2005-02-15

Smith, Matthew (Department: 2825)

Computer-aided design and analysis of circuits and semiconductor

Nanotechnology related integrated circuit design

C716S030000

Reexamination Certificate

active

06857114

ABSTRACT:
An improved profileometry data collection and analysis system employing software that performs clustering analysis on library data stored in memory that represent semiconductor chip wafer profiles, for use in matching real-time data signals from data collected by profileometry instruments. To better perform a match in real-time between the incoming real-time data signals and the profile library data, cluster analysis is performed on the library data to partition the library data into clusters, and to extract representative cluster data points of the clusters. The representatives of the clusters are stored in primary memory (e.g., RAM), while the data forming the clusters are stored in secondary memory (e.g., a hard drive). A real-time data signal is then first compared to the representative cluster data points, and when a match is made with a particular representative cluster data point, the cluster associated with the representative cluster data point is loaded from secondary memory into primary memory. Next a further search is made with the incoming real-time data signal to find the closest match to the data in the cluster. In this way the entire library data does not have to be searched sequentially, and the entire library does not have to reside in primary memory in order to be quickly searched, which both conserves time and primary memory. Techniques are disclosed to further refine special cases of points residing on the boundary of the cluster. In a preferred embodiment, the partitioning method for the cluster is based on the T. Gonzalez algorithm.

REFERENCES:
patent: 5486998 (1996-01-01), Corso
patent: 6337654 (2002-01-01), Richardson et al.
T. Feder et al., “Optimal Algorithms for Approximate Clustering”, Proceedings of the 20th ACM Symposium on Theory of Computing, pp. 434-444, 1988.
T. Gonzalez, “Clustering to Minimize the Maximum Intercluster Distance”, Theoretical Computer Science, vol. 38, pp. 293-306, 1985.

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