Kla Tencor Corporation

Tai-Kam Ng

Martin Plihal

Patrick Huet

Chien-Huei (Adam) Chen

Erfan Soltanmohammadi

Chris Maher

John Raymond Jordan, Iii

Sang Hyun Lee

Defects of interest can be captured by a classifier. Images of a semiconductor wafer can be received at a deep learning classification module. These images can be sorted into soft decisions with the deep learning classification module. A class of the defect of interest for an image can be determined from the soft decisions. The deep learning classification module can be in electronic communication with an optical inspection system or other types of semiconductor inspection systems.

Creating and tuning a classifier to capture more defects of interest during inspection

Methods and systems for decision tree construction for automatic classification of defects on semiconductor wafers are provided. One method includes creating a decision tree for classification of defects detected on a wafer by altering one or more floating trees in the decision tree. The one or more floating trees are sub-trees that are manipulated as individual units. In addition, the method includes classifying the defects detected on the wafer by applying the decision tree to the defects.

Decision tree construction for automatic classification of defects on semiconductor wafers

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San Jose, CA, United States of America

Tai-Kam Ng