Applied Materials, Inc.

Amos Dor

Maya M Radzinski

Israel Beinglass

Yifah Gavra

Amir Feili

One embodiment of the present invention is a process tool optimization system that includes: (a) a data mining engine that analyzes end-of-line yield data to identify one or more process tools associated with low yield; and (b) in response to output from the analysis, analyzes process tool data from the one or more process tools to identify one or more process tool parameters associated with the low yield.

Correlation of end-of-line data mining with process tool data mining

A method () for determining a response to a wafer manufacturing process problem includes the steps of generating a wafer map signature () containing defect attributes to be identified and retrieving () one or more wafer map signatures from a database. The wafer map signature to be identified is matched () with the one or more wafer map signatures from the database in order to help find a root cause of a defect in a wafer manufacturing process. This provides at least the advantage that a signature matching capability allows an Operator to more accurately identify a root manufacturing cause of a wafer defect. Furthermore, instant feedback to the wafer manufacturing process can be provided to facilitate rapid correction of manufacturing errors.

System to identify a wafer manufacturing problem and method therefor

A method and associated apparatus for creating a defect knowledge library containing case study information of wafer defects on semiconductor wafers. The method comprises creating a database entry that contains a case study of a specific defect including defect information that comprises one or more defect images and storing the database entry for subsequent access. The database entries are stored on a server and are accessible by a plurality of clients.

Defect knowledge library

A method and apparatus for providing communication between a defect source identifier and a tool data collection and control system. The defect source identifier collects wafer data until a defect is identified. Upon identification of a defect, a request is sent to the tool data collection and control system to request data of the tool parameters at the time the defect occurred. The tool data collection and control system retrieves the tool parameters and communicates them to the defect source identifier through a network. The tool parameters are processed by the defect source identifier to extract certain wafer data. The selected wafer data is communicated to the tool data collection and control system and is used to execute a prediction model to predict failure possible of the tool elements.

Method and apparatus for providing communication between a defect source identifier and a tool data collection and control system

A method and associated apparatus of analyzing defects on semiconductor wafers. The method includes identifying defects on the semiconductor wafer. Defect inspection information is created within a defect source identifier client. The defect inspection information containing information regarding the identified defects. The defect inspection information is transmitted through a network to a defect source identifier server. Defect source information is derived at the defect source identifier server in response to the defect inspection information. The defect source information is transmitted from the defect source identifier server to the defect source identifier client. The defect source information is utilized at the defect source identifier client. In one aspect, the utilizing the defect solution information involves displaying defect solutions to the defect at the defect source identifier client in response to the defect solution information. In another aspect, utilizing the defect solution information involves altering the operation of the wafer processing system.

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Sunnyvale, CA, United States of America

Amos Dor