Kla Tencor Corporation

Kla-tenfor Corp.

Li He

Sankar Venkataraman

Huajun Ying

Mohan Mahadevan

Chien-Huei Adam Chen

John Raymond Jordan, Iii

Harsh Sinha

Martin Plihal

Brian Duffy

Kris Bhaskar

Scott Allen Young

Jing Zhang

Laurent Karsenti

Hedong Yang

Oksen Toros Baris

Arjun Hegde

Shaoyu Lu

Amitoz Singh Dandiana

Sinha Harsh

Anadi Bhatia

Ramakanth Ramini

Hung Nien

Ian Riley

John R Jordan

Michael Kowalski

Methods and systems for accelerated training of a machine learning based model for semiconductor applications are provided. One method for training a machine learning based model includes acquiring information for non-nominal instances of specimen(s) on which a process is performed. The machine learning based model is configured for performing simulation(s) for the specimens. The machine learning based model is trained with only information for nominal instances of additional specimen(s). The method also includes re-training the machine learning based model with the information for the non-nominal instances of the specimen(s) thereby performing transfer learning of the information for the non-nominal instances of the specimen(s) to the machine learning based model.

Accelerated training of a machine learning based model for semiconductor applications

Methods and systems for training a machine learning model using synthetic defect images are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a graphical user interface (GUI) configured for displaying one or more images for a specimen and image editing tools to a user and for receiving input from the user that includes one or more alterations to at least one of the images using one or more of the image editing tools. The component(s) also include an image processing module configured for applying the alteration(s) to the at least one image thereby generating at least one modified image and storing the at least one modified image in a training set. The computer subsystem(s) are configured for training a machine learning model with the training set in which the at least one modified image is stored.

Training a machine learning model with synthetic images

Autoencoder-based, semi-supervised approaches are used for anomaly detection. Defects on semiconductor wafers can be discovered using these approaches. The model can include a variational autoencoder, such as a one that includes ladder networks. Defect-free or clean images can be used to train the model that is later used to discover defects or other anomalies.

Semi-supervised anomaly detection in scanning electron microscope images

Methods and systems for detecting and classifying defects on a specimen are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a neural network configured for detecting defects on a specimen and classifying the defects detected on the specimen. The neural network includes a first portion configured for determining features of images of the specimen generated by an imaging subsystem. The neural network also includes a second portion configured for detecting defects on the specimen based on the determined features of the images and classifying the defects detected on the specimen based on the determined features of the images.

Unified neural network for defect detection and classification

Defect classification includes acquiring one or more images of a specimen, receiving a manual classification of one or more training defects based on one or more attributes of the one or more training defects, generating an ensemble learning classifier based on the received manual classification and the attributes of the one or more training defects, generating a confidence threshold for each defect type of the one or more training defects based on a received classification purity requirement, acquiring one or more images including one or more test defects, classifying the one or more test defects with the generated ensemble learning classifier, calculating a confidence level for each of the one or more test defects with the generated ensemble learning classifier and reporting one or more test defects having a confidence level below the generated confidence threshold via the user interface device for manual classification.

Method and system for defect classification

Methods and systems for classifying defects detected on a specimen with an adaptive automatic defect classifier are provided. One method includes creating a defect classifier based on classifications received from a user for different groups of defects in first lot results and a training set of defects that includes all the defects in the first lot results. The first and additional lot results are combined to create cumulative lot results. Defects in the cumulative lot results are classified with the created defect classifier. If any of the defects are classified with a confidence below a threshold, the defect classifier is modified based on a modified training set that includes the low confidence classified defects and classifications for these defects received from a user. The modified defect classifier is then used to classify defects in additional cumulative lot results.

Adaptive automatic defect classification

Defect classification includes acquiring one or more images of a specimen including multiple defects, grouping the defects into groups of defect types based on the attributes of the defects, receiving a signal from a user interface device indicative of a first manual classification of a selected number of defects from the groups, generating a classifier based on the first manual classification and the attributes of the defects, classifying, with the classifier, one or more defects not manually classified by the manual classification, identifying the defects classified by the classifier having the lowest confidence level, receiving a signal from the user interface device indicative of an additional manual classification of the defects having the lowest confidence level, determining whether the additional manual classification identifies one or more additional defect types not identified in the first manual classification, and iterating the procedure until no new defect types are found.

Method and system for iterative defect classification

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San Jose, CA, United States of America

Li He