Kla-tencor Technologies Corporation

Kla Tencor Corporation

Kla Corporation

Tong Huang

Lisheng Gao

Jianxin Zhang

Kenong Wu

Yong Zhang

Patrick Huet

Xuguang Jiang

Michal Kowalski

Martin Plihal

Ashok V Kulkarni

David Y Wang

Sharon McCauley

Cecelia Anne Campochiaro

Tommaso Torelli

Cho Huak Teh

Sandeep Bhagwat

David Winslow Randall

Pavan Kumar Perali

Ariel Tribble

Ashok Varadarajan

Mike Van Riet

Vivekanand Kini

Stewart Hill

Dominic David

Jie Gong

Lalita A Balasubramanian

Jonathan Oakley

Maruti Shanbhag

Wei Kang

Faisal Omer

Yiyu Zhang

N R Girish

Chiuman Yeung

Vadim Romanovski

Michael Gordon Scott

Adam Chien-Huei Chen

Bo Magluyan

Kevin Yeung

Methods and systems for setting up alignment of a specimen are provided. One system includes computer subsystem(s) configured for acquiring two-dimensional (2D) images generated from output of a detector of an output acquisition subsystem at template locations in corresponding areas of printed instances on a specimen. The computer subsystem(s) determine offsets in x and y directions between the template locations using the 2D images and determine an angle of the specimen with respect to the output acquisition subsystem based on the offsets. If the angle is greater than a predetermined value, the computer subsystem(s) rotate the specimen and repeat the steps described above. If the angle is less than the predetermined value, the computer subsystem(s) store one of the 2D images for alignment of the specimen in a process performed on a specimen. The 2D images may include multi-mode images, which may be fused prior to determining the offsets.

Alignment of a specimen for inspection and other processes

Methods and systems for detecting defects on a specimen are provided. One system includes a first deep learning (DL) network configured for filtering nuisances from defect candidates detected on a specimen. Output of the first DL network includes a first subset of the defect candidates not filtered as the nuisances. The system also includes a second DL network configured for filtering nuisances from the first subset of the defect candidates. Computer subsystem(s) input high resolution images acquired for the first subset of the defect candidates into the second DL network. Output of the second DL network includes a final subset of the defect candidates not filtered as the nuisances. The computer subsystem(s) designate the defect candidates in the final subset as defects on the specimen and generate results for the defects.

Deep learning networks for nuisance filtering

A system, method, and computer program product are provided for identifying fabricated component defects using a local adaptive threshold. In use, images are received for target and reference components of a fabricated device. Additionally, a difference image is generated from the target and reference component images, and defect candidates for the target component are identified from the difference image. Further, for each of the identified defect candidates at a location in the difference image: a threshold is determined based on a local area surrounding the location of the defect candidate, and a signal at the location of the defect candidate is compared to the threshold to determine whether the defect candidate is a defect.

System, method and computer program product for identifying fabricated component defects using a local adaptive threshold

Systems and methods for detecting defects on a wafer are provided. One method includes generating output for a wafer by scanning the wafer with a dark field inspection system. The method also includes generating first image data for the wafer using the output and a first cell size and second image data for the wafer using the output and a second cell size. In addition, the method includes combining the first image data and the second image data corresponding to substantially the same locations on the wafer thereby creating additional image data for the wafer. The method further includes detecting defects on the wafer using the additional image data.

Single die inspection on a dark field inspection tool

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

A method for constructing a distance histogram for nearest neighbor classification. A training sample is determined for each of two classes. For each defect, a distance is measured in the feature space between the defect and the training sample for each of the classes. All of the distances for a given defect are normalized by dividing each distance for the given defect by the sum of all of the distances for the given defect. A histogram is constructed by plotting a chart of the normalized distances versus the number of defects having the distances. A threshold bar is placed on the center of the histogram to construct a normal nearest neighbor classifier. The threshold bar can be adjusted to the left or to the right to construct a weighted nearest neighbor classifier.

Distance histogram for nearest neighbor defect classification

Methods and systems for generating an inspection process for an inspection system are provided. One computer implemented method includes generating inspection data for a selected defect on a specimen at different values of one or more image acquisition parameters of the inspection system. The method also includes determining which of the different values produces the best inspection data for the selected defect. In addition, the method includes selecting the different values determined to produce the best inspection data as values of the one or more image acquisition parameters to be used for the inspection process.

Methods and systems for generating an inspection process for an inspection system

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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San Jose, CA, United States of America

Tong Huang