Automated defect spatial signature analysis for semiconductor manufacturing process
First Claim
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1. A method of performing automated defect spatial signature analysis on a data set representing defect coordinates and wafer processing information, comprising the steps of:
- categorizing data from the data set into a plurality of high level event categories including global events, curvilinear events, amorphous events, and micro-structure events;
classifying the categorized data contained in each high level category into user-labeled signature events; and
correlating the categorized, classified signature events to a present or incipient anomalous process condition.
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Abstract
An apparatus and method for performing automated defect spatial signature alysis on a data set representing defect coordinates and wafer processing information includes categorizing data from the data set into a plurality of high level categories, classifying the categorized data contained in each high level category into user-labeled signature events, and correlating the categorized, classified signature events to a present or incipient anomalous process condition.
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Citations
25 Claims
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1. A method of performing automated defect spatial signature analysis on a data set representing defect coordinates and wafer processing information, comprising the steps of:
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categorizing data from the data set into a plurality of high level event categories including global events, curvilinear events, amorphous events, and micro-structure events; classifying the categorized data contained in each high level category into user-labeled signature events; and correlating the categorized, classified signature events to a present or incipient anomalous process condition. - View Dependent Claims (2, 3, 4, 5)
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6. A method of performing automated defect spatial signature analysis on a data set representing defect coordinates and wafer processing information, comprising the steps of:
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forming a density image, ρ
(x,y), from the data set;parsing the density image into two categories based on defect density values;
low-density, potentially random events and higher-density, potentially clustered events, thereby segmenting random defect distributions from other signature type events;classifying the data contained in each category into user-labeled signature events; and correlating the categorized, classified signature events to a present or incipient anomalous process condition. - View Dependent Claims (7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. An apparatus for performing automated defect spatial signature analysis on a data set representing defect coordinates and wafer processing information, comprising:
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means for categorizing data from the data set into a plurality of high level categories including global events, curvilinear events, amorphous events, and micro-structure events; means for classifying the categorized data contained in each high level category into user-labeled signature events; and means for correlating the categorized, classified signature events to a present or incipient anomalous process condition. - View Dependent Claims (19, 20, 21, 22, 23, 24, 25)
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Specification