Kla Tencor Corporation

Harsh Sinha

Christopher Sears

Hong Xiao

Xinrong Jiang

Sankar Venkataraman

Li He

John Raymond Jordan, Iii

David Trease

David Kaz

Wei Ye

Michael John Van Riet

Chien-Huei Adam Chen

Cecelia Anne Campochiaro

Huajun Ying

Oksen Toros Baris

Rohit Bothra

Huina Xu

Benjamin James Thomas Clarke

Dmitry Spivak

An electron-optical system for inspecting or reviewing an edge portion of a sample includes an electron beam source configured to generate one or more electron beams, a sample stage configured to secure the sample and an electron-optical column including a set of electron-optical elements configured to direct at least a portion of the one or more electron beams onto an edge portion of the sample. The system also includes a sample position reference device disposed about the sample and a guard ring device disposed between the edge of the sample and the sample position reference device to compensate for one or more fringe fields. One or more characteristics of the guard ring device are adjustable. The system also includes a detector assembly configured to detect electrons emanating from the surface of the sample.

Method and system for edge-of-wafer inspection and review

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

One embodiment relates to a method for automated review of defects detected in a defective die on the target substrate. The method includes: performing an automated review of the defects using an secondary electron microscope (SEM) so as to obtain electron-beam images of the defects; performing an automated classification of the defects into types based on morphology of the defects as determined from the electron-beam images; selecting defects of a specific type for automated energy-dispersive x-ray (EDX) review; and performing the automated EDX review on the defects of the specific type. In addition, automated techniques are disclosed for obtaining an accurate reference so as to improve the usefulness of the EDX results. Furthermore, an automated method of classifying the defects based on the EDX results is disclosed which provides a final pareto that combines both morphological and elemental information. Other embodiments, aspects and features are also disclosed.

Automated decision-based energy-dispersive x-ray methodology and apparatus

One embodiment relates to a method of preparation of a sample of a substrate for sub-surface review using a scanning electron microscope apparatus. A defect at a location indicated in a first results file is re-detected, and the location of the defect is marked with at least one discrete marking point having predetermined positioning relative to the location of the defect. The location of the defect may be determined relative to the design for the device, and a cut location and a cut angle may be determined in at least a partly-automated manner using that information. Another embodiment relates to a system for preparing a sample for sub-surface review. Another embodiment relates to a method for marking a defect for review on a target substrate. Other embodiments, aspects and feature are also disclosed.

Systems and methods for preparation of samples for sub-surface defect review

Growing community of inventors

Santa Clara, CA, United States of America

Harsh Sinha