Kla Tencor Corporation

Kla Corporation

Kla-tencor Technologies Corporation

Tommaso Torelli

Bradley Ries

Lisheng Gao

Mohan Mahadevan

Himanshu Vajaria

Vineethanand Hariharan

Muthukrishnan Sankar

Stilian Pandev

Brian Duffy

Kenong Wu

Tao Luo

Michael John Van Riet

Tong Huang

Chris W Lee

Cho Huak Teh

Michal Kowalski

Shabnam Ghadar

Jianxin Zhang

Dominic David

Lalita A Balasubramanian

Jonathan Oakley

Chiuman Yeung

Michael Gordon Scott

A system is disclosed, in accordance with one or more embodiment of the present disclosure. The system may include a controller including one or more processors configured to execute a set of program instructions. The set of program instructions may be configured to cause the processors to: receive images of a sample from a characterization sub-system; identify target clips from patch clips; prepare processed clips based on the target clips; generate encoded images by transforming the processed clips; sort the encoded images into a set of clusters; display sorted images from the set of clusters; receive labels for the displayed sorted images; determine whether the received labels are sufficient to train a deep learning classifier; and upon determining the received labels are sufficient to train the deep learning classifier, train the deep learning classifier via the displayed sorted images and the received labels.

Characterization system and method with guided defect discovery

Methods and systems for monitoring process tool conditions are disclosed. The method combines single wafer, multiple wafers within a single lot and multiple lot information together statistically as input to a custom classification engine that can consume single or multiple scan, channel, wafer and lot to determine process tool status.

Wafer and lot based hierarchical method combining customized metrics with a global classification methodology to monitor process tool condition at extremely high throughput

Computer-implemented methods, computer-readable media, and systems for selecting one or more parameters for inspection of a wafer are provided.

Selecting one or more parameters for inspection of a wafer

Methods and devices are disclosed for automated detection of a status of wafer fabrication process based on images. The methods advantageously use segment masks to enhance the signal-to-noise ratio of the images. Metrics are then calculated for the segment mask variations in order to determine one or more combinations of segment masks and metrics that are predictive of a process non-compliance. A model can be generated as a result of the process. In another embodiment, a method uses a model to monitor a process for compliance.

Automated image-based process monitoring and control

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

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Berkeley, CA, United States of America

Tommaso Torelli