Dimensionality Reduction and Clustering of Wafer-Level Data to Optimize Use of Failure Analysis Resources
Dimensionality Reduction and Clustering of Wafer-Level Data to Optimize Use of Failure Analysis Resources
Tuesday, November 14, 2023: 10:50 AM
104 A-B (Phoenix Convention Center)
Summary:
A methodology for clustering wafer data by their signatures in a high-dimensional feature space and determining the most important features for a given cluster. By pooling information gathered from all wafers in a given cluster, it is easier to determine the cause of observed feature signatures on wafers. This enables more efficient use of failure analysis (FA) resources, as they can be focused on clusters with no existing root cause information.
A methodology for clustering wafer data by their signatures in a high-dimensional feature space and determining the most important features for a given cluster. By pooling information gathered from all wafers in a given cluster, it is easier to determine the cause of observed feature signatures on wafers. This enables more efficient use of failure analysis (FA) resources, as they can be focused on clusters with no existing root cause information.