Defect Detection on Semiconductor Wafers by Distribution Analysis
This work addresses defect detection in semiconductor manufacturing, which is critical for quality control, but it appears incremental as it builds on existing classification algorithms.
The authors tackled defect detection on semiconductor wafers by proposing a classification method based on distribution analysis, which was successfully applied to real-world data from nearly 100,000 chips across several product types, achieving good to excellent detection quality.
A method for object classification that is based on distribution analysis is proposed. In addition, a method for finding relevant features and the unification of this algorithm with another classification algorithm is proposed. The presented classification algorithm has been applied successfully to real-world measurement data from wafer fabrication of close to hundred thousand chips of several product types. The presented algorithm prefers finding the best rater in a low-dimensional search space over finding a good rater in a high-dimensional search space. Our approach is interesting in that it is fast (quasi-linear) and reached good to excellent prediction or detection quality for real-world wafer data.