Kla-Tencor Technologies Corporation

Kla Tencor Corporation

Michal Kowalski

Lisheng Gao

Ashok V Kulkarni

Kenong Wu

David Winslow Randall

Ariel Tribble

Tong Huang

Ingrid B Peterson

Jianxin Zhang

Ramon Ynzunza

Kourosh Nafisi

Jason Z Lin

Hong Chen

Subramanian Balakrishnan

Patrick Huet

Michael John Van Riet

Chien-Huei (Adam) Chen

Chris Maher

Sharon McCauley

Stephanie Chen

Cecelia Anne Campochiaro

Tommaso Torelli

Cho Huak Teh

Sandeep Bhagwat

Vivekanand Kini

Barry Becker

Dominic David

Lalita A Balasubramanian

Jonathan Oakley

Suryanarayana Tummala

Maruti Shanbhag

Chiuman Yeung

Michael Gordon Scott

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 computer-implemented methods are provided. One method for sorting defects in a design pattern of a reticle includes searching for defects of interest in inspection data using priority information associated with individual defects in combination with one or more characteristics of a region proximate the individual defects. The priority information corresponds to modulation levels associated with the individual defects. The inspection data is generated by comparing images of the reticle generated for different values of a lithographic variable. The images include at least one reference image and at least one modulated image. A composite reference image can be generated from two or more reference images. The method also includes assigning one or more identifiers to the defects of interest. The identifier(s) may include, for example, a defect classification and/or an indicator identifying if the defects of interest are to be used for further processing.

Computer-implemented methods for detecting and/or sorting defects in a design pattern of a reticle

Methods and systems for generating information to be used for selecting values for parameter(s) of a detection algorithm are provided. One method includes without user intervention performing a scan of an area of a wafer using an inspection system and default values for parameter(s) of a detection algorithm to detect defects on the wafer. The method also includes selecting a portion of the defects from results of the scan based on a predetermined maximum number of total defects to be used for selecting values for the parameter(s) of the detection algorithm. The method further includes storing information, which includes values for the parameter(s) of the detection algorithm determined for the defects in the portion. The information can be used to select the values for the parameter(s) of the detection algorithm to be used for the inspection recipe without performing an additional scan of the wafer subsequent to the scan.

Methods and systems for generating information to be used for selecting values for one or more parameters of a detection algorithm

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

Growing community of inventors

Santa Cruz, CA, United States of America

Michal Kowalski