Kla-Tencor Technologies Corporation

Kla Tencor Corporation

Jianxin Zhang

Lisheng Gao

Tong Huang

Kenong Wu

Michal Kowalski

Ashok V Kulkarni

Ellis Chang

Patrick Huet

Chien-Huei (Adam) Chen

Yan Xiong

Sharon McCauley

Tommaso Torelli

Cecelia Anne Campochiaro

David Winslow Randall

Sandeep Bhagwat

Cho Huak Teh

Vivekanand Kini

Tsung-Pao Fang

Ariel Tribble

Lalita A Balasubramanian

Jonathan Oakley

Maruti Shanbhag

Dominic David

Chiuman Yeung

Michael Gordon Scott

Bo Magluyan

Kevin Yeung

Various computer-implemented methods for classifying defects on a specimen are provided. One method includes assigning individual defects detected on the specimen to defect groups based on one or more characteristics of the individual defects. The method also includes displaying information about the defect groups to a user. In addition, the method includes allowing the user to assign a classification to each of the defect groups. Systems configured to classify defects on a specimen are also provided. One system includes program instructions executable on a processor for assigning individual defects detected on the specimen to defect groups based on one or more characteristics of the individual defects. The system also includes a user interface configured for displaying information about the defect groups to a user and allowing the user to assign a classification to each of the defect groups.

Computer-implemented methods and systems for classifying defects on a specimen

Various methods and systems for determining a defect criticality index (DCI) for defects on wafers are provided. One computer-implemented method includes determining critical area information for a portion of a design for a wafer surrounding a defect detected on the wafer by an inspection system based on a location of the defect reported by the inspection system and a size of the defect reported by the inspection system. The method also includes determining a DCI for the defect based on the critical area information, a location of the defect with respect to the critical area information, and the reported size of the defect.

Methods and systems for determining a defect criticality index for defects on wafers

Disclosed are methods and apparatus for efficiently setting up and maintaining a defect classification system. In general terms, the setup procedure optionally includes automatically grouping a set of provided defects and presenting a representative set from each defect group to the user for classification. After the initial manual classification of the representative defects, the setup procedure includes an automatic procedure for classifying the non-reviewed or unclassified defects based on the manual class codes from the user-reviewed defects. After the automatic classification operation, the user may also be presented with defects from each class which may require re-classification. In particular embodiments, the user is iteratively presented with defects which have classifications that are suspect, which are near classification boundaries, or have classifications that have a low confidence level until each class is pure or contains a same type of defect classes as assigned by the user.

Automatic supervised classifier setup tool for semiconductor defects

Hybrid methods for classifying defects in semiconductor manufacturing are provided. The methods include applying a flexible sequence of rules for defects to inspection data. The sequence of rules includes deterministic rules, statistical rules, hybrid rules, or some combination thereof. The rules included in the sequence may be selected by a user using a graphical interface. The method also includes classifying the defects based on results of applying the sequence of rules to the inspection data.

Flexible hybrid defect classification for semiconductor manufacturing

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Santa Clara, CA, United States of America

Jianxin Zhang