Kla-Tencor Technologies Corporation

Kla Tencor Corporation

David R Bakker

Christopher F Bevis

Ian R Smith

Gabor D Toth

Saibal Banerjee

Amir Azordegan

Prashant Aji

James L Belliveau

Chacko C Neroth

Bharat Marathe

Disclosed are methods and apparatus for automatically organizing and/or analyzing a plurality of defect images without first providing a predefined set of classified images (herein referred to as a training set). In other words, sorting is not based on a training set or predefined classification codes for such defect images. In one embodiment, the defect images each include associated identifying data, such as a fabrication identifier, lot number, wafer number, and layer identifier. Initially, the defect images are sorted according to at least a portion of the associated identifying data into a plurality of 'identifying data groups' or image families. The defect data in each identifying data group is then automatically sorted according to defect appearance. That is, similar defect images are associated with a single bin and similar bins are associated with other similar bins. For example, similar bins are arranged next to each other within a graphical user interface (GUI). A representative feature vector (herein referred to as a 'centroid') is then associated with each bin. The centroid generally represents the images within the particular bin. A search for images that &#x201c;look like&#x201d; a specified target image may then be efficiently performed on a particular identifying data group using the centroids of each bin. The target image's feature vector is compared with the centroid of each bin that is within the same identifying data group or image family as the target image. The techniques of the present invention may also be applied to wafer maps, as well as defect images.

Apparatus and methods for searching through and analyzing defect images and wafer maps

A method and system that optionally allows a user to view image defects organized by natural groupings based on features of the images. The natural groupings make it easier for the user to organize some or all of the images into classes in a training set of images. A feature vector is extracted from each image in the training set and stored, along with its user-specified class, for use by an automatic classifier software module. The automatic classifier uses the stored feature vectors and classes to automatically classify images not in the training set. If the automatically classified images do not match images manually classified by the user, the user modifies the training set until a better result is obtained from the automatic classifier. The system can provide feedback to an inspection system designed to aid in the setup and fine-tuning of the inspection system.

Power assisted automatic supervised classifier creation tool for semiconductor defects

Methods and apparatus for efficiently analyzing defects in-line on a wafer by wafer basis are provided. In general terms, embodiments of the present invention provide a simple interface for setting up the entire inspection and defect analysis process in a single set up procedure. The apparatus includes an inspection station for inspecting a specimen for potential defects and a review station for analyzing a sample of the potential defects to determine a classification of such potential defects. The apparatus further includes a computer system having an application interface operable to allow a user to set up the inspection station and the review station during a same setup phase so as to allow the inspection station and the review station to then operate automatically to provide defect information for one or more specimens based on the user set up.

Single tool defect classification solution

An apparatus for recording a series of images of a sample over a period of time while varying at least one image parameter. An electron microscope captures images of the sample and also varies the at least one image parameter. A controller triggers the electron microscope to sense multiple images of the sample and also controls the electron microscope to vary the at least one image parameter. An image recorder receives the sensed multiple images and also stores the sensed multiple images as the series of images. A display unit displays the series of images.

Motion picture output from electron microscope

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Cupertino, CA, United States of America

David R Bakker