Kla Tencor Corporation

John Raymond Jordan, Iii

Sankar Venkataraman

Kris Bhaskar

Laurent Karsenti

Li He

Harsh Sinha

Mehrdad Nikoonahad

Bjorn Brauer

Ashok V Kulkarni

Eliezer Rosengaus

Eugene Shifrin

Keith B Wells

Patrick Huet

Chien-Huei (Adam) Chen

Chien-Huei Adam Chen

Chris Maher

Huajun Ying

Christopher Maher

Vijayakumar Ramachandran

Oksen Toros Baris

Graham Michael Lynch

Tai-Kam Ng

Yair Carmon

Chwen-Jiann Fang

Roni Miller

A defect detection method includes acquiring a reference image; selecting a target region of the reference image; identifying, based on a matching metric, one or more comparative regions of the reference image corresponding to the target region; acquiring a test image; masking the test image with the target region of the reference image and the one or more comparative regions of the reference image; defining a defect threshold for the target region in the test image based on the one or more comparative regions in the test image; and determining whether the target region of the test image contains a defect based on the defect threshold.

Systems and methods for region-adaptive defect detection

Methods and systems for detecting defects on a specimen are provided. One system includes a generative model. The generative model includes a non-linear network configured for mapping blocks of pixels of an input feature map volume into labels. The labels are indicative of one or more defect-related characteristics of the blocks. The system inputs a single test image into the generative model, which determines features of blocks of pixels in the single test image and determines labels for the blocks based on the mapping. The system detects defects on the specimen based on the determined labels.

Single image detection

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

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 decision tree construction for automatic classification of defects on semiconductor wafers are provided. One method includes creating a decision tree for classification of defects detected on a wafer by altering one or more floating trees in the decision tree. The one or more floating trees are sub-trees that are manipulated as individual units. In addition, the method includes classifying the defects detected on the wafer by applying the decision tree to the defects.

Decision tree construction for automatic classification of defects on semiconductor wafers

Systems and methods for detecting defects on a wafer are provided. One method includes determining locations of all instances of a weak geometry in a design for a wafer. The locations include random, aperiodic locations. The weak geometry includes one or more features that are more prone to defects than other features in the design. The method also includes scanning the wafer with a wafer inspection system to thereby generate output for the wafer with one or more detectors of the wafer inspection system. In addition, the method includes detecting defects in at least one instance of the weak geometry based on the output generated at two or more instances of the weak geometry in a single die on the wafer.

Detecting defects on a wafer

A high throughput surface inspection system with enhanced detection sensitivity is described. The acquired data is processed in real time at a rate of below 50 MHz thereby reducing the cost for data processing. Anomalies are detected and verified by comparing adjacent repeating patterns and the height of the surface is monitored and corrected dynamically to reduce misregistration errors between adjacent repeating patterns. Local thresholds employing neighborhood information are used for detecting and verifying the presence of anomalies. The sampled point spread function of the combined illumination and collection system is exploited for anomaly detection and verification.

Surface inspection system

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Mountain View, CA, United States of America

John Raymond Jordan, Iii