Kla Tencor Corporation

Kla Corporation

Bradley Ries

Himanshu Vajaria

Tommaso Torelli

Mohan Mahadevan

Shabnam Ghadar

Vineethanand Hariharan

Muthukrishnan Sankar

Sina Jahanbin

Stilian Pandev

Brian Duffy

Yong Zhang

Laurent Karsenti

Kuljit Virk

Anuj Pandey

Rahul Lakhawat

Sasha Smekhov

Asaf J Elron

Ruslan Berdichevsky

Ron Dekel

Emanuel Garbin

Yakir Gorski

Oriel Ben Shmuel

Shlomi Fenster

Oren Dovrat

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

A context-based inspection system is disclosed. The system may include an optical imaging sub-system. The system may further include one or more controllers communicatively coupled to the optical imaging system. The one or more controllers may be configured to: receive one or more reference images; receive one or more test images of a sample; generate one or more probabilistic context maps during inspection runtime using an unsupervised classifier; provide the generated one or more probabilistic context maps to a supervised classifier during the inspection runtime; and apply the supervised classifier to the received one or more test images to identify one or more DOIs on the sample.

Context-based defect inspection

A system for analyzing a sample includes an inspection sub-system and at least one controller. The inspection sub-system is configured to scan a sample to collect a first plurality of sample images having a first image resolution. The controller is configured to generate a defect list based on the first plurality of sample images. The controller is further configured to input images corresponding to the defect list into a neural network that is trained with source data including sample images having the first image resolution and sample images having a second image resolution higher than the first image resolution. The controller is further configured to generate a second plurality of sample images with the neural network based on the images corresponding to the defect list, where the second plurality of sample images have the second image resolution and correspond to the defect list.

Super-resolution defect review image generation through generative adversarial networks

Feature extraction and classification is used for process window monitoring. A classifier, based on combinations of metrics of masked die images and including a set of significant combinations of one or more segment masks, metrics, and wafer images, is capable of detecting a process non-compliance. A process status can be determined using a classifier based on calculated metrics. The classifier may learn from nominal data.

Feature selection and automated process window monitoring through outlier detection

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

Shadow-grams are used for edge inspection and metrology of a stacked wafer. The system includes a light source that directs collimated light at an edge of the stacked wafer, a detector opposite the light source, and a controller connected to the detector. The stacked wafer can rotate with respect to the light source. The controller analyzes a shadow-gram image of the edge of the stacked wafer. Measurements of a silhouette of the stacked wafer in the shadow-gram image are compared to predetermined measurements. Multiple shadow-gram images at different points along the edge of the stacked wafer can be aggregated and analyzed.

Automated inline inspection and metrology using shadow-gram images

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

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San Jose, CA, United States of America

Bradley Ries