Kla Tencor Corporation

Kla-tencor Technologies Corporation

Kla Corporation

Chris Maher

Martin Plihal

Brian Duffy

Chien-Huei (Adam) Chen

Hong Chen

Santosh Bhattacharyya

Patrick Huet

Michael John Van Riet

Erfan Soltanmohammadi

Stephanie Chen

Barry Becker

Suryanarayana Tummala

Andrew V Hill

Ashok V Kulkarni

Eliezer Rosengaus

Kris Bhaskar

Jason Z Lin

Subramanian Balakrishnan

Mohan Mahadevan

Yong Zhang

Prasanti Uppaluri

Gordon Rouse

Aneesh Khullar

John Raymond Jordan, Iii

Cecelia Anne Campochiaro

Chetana Bhaskar

Harish P Hiriyannaiah

Michal Kowalski

Thomas Trautzsch

Younus Vora

Lalita A Balasubramanian

Mohit Jani

Tai-Kam Ng

Alpa Kohli

Verlyn Fischer

Ping Ding

Lalita Balasubramanian

Systems and methods for detecting defect candidates on a specimen are provided. One method includes, after scanning of at least a majority of a specimen is completed, applying one or more segmentation methods to at least a substantial portion of output generated during the scanning thereby generating two or more segments of the output. The method also includes separately detecting outliers in the two or more segments of the output. In addition, the method includes detecting defect candidates on the specimen by applying one or more predetermined criteria to results of the separately detecting to thereby designate a portion of the detected outliers as the defect candidates.

Defect candidate generation for inspection

Methods and systems for setting up inspection of a specimen with design and noise based care areas are provided. One system includes one or more computer subsystems configured for generating a design-based care area for a specimen. The computer subsystem(s) are also configured for determining one or more output attributes for multiple instances of the care area on the specimen, and the one or more output attributes are determined from output generated by an output acquisition subsystem for the multiple instances. The computer subsystem(s) are further configured for separating the multiple instances of the care area on the specimen into different care area sub-groups such that the different care area sub-groups have statistically different values of the output attribute(s) and selecting a parameter of an inspection recipe for the specimen based on the different care area sub-groups.

And noise based care areas

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 monitoring time-varying classification performance are disclosed. A method may include, but is not limited to: receiving one or more signals indicative of one or more properties of one or more samples from one or more scanning inspection tools; determining populations of one or more defect types for the one or more samples according an application of one or more classification rules to the one or more signals received from the one or more scanning inspection tools; determining populations of the one or more defect types for the one or more samples using one or more high-resolution inspection tools; and computing one or more correlations between populations of one or more defect types for one or more samples determined from application of one or more classification rules applied to one or more signals received from the one or more scanning inspection tools and populations of the one or more defect types for the one or more samples determined using the one or more high-resolution inspection tools.

Monitoring of time-varying defect classification performance

Computer-implemented methods, carrier media, and systems for creating a defect sample for use in selecting one or more parameters of an inspection recipe are provided. One method includes separating defects into bins based on regions in which the defects are located, defect types, and values of the defects for parameter(s) of a detection algorithm. The method also includes determining a number of the defects to be selected from each bin by distributing a user-specified target number of defects across the bins. In addition, the method includes selecting defects from the bins based on the determined numbers thereby creating a defect sample for use in selecting values of parameter(s) of the detection algorithm for use in the inspection recipe.

Computer-implemented methods, carrier media, and systems for creating a defect sample for use in selecting one or more parameters of an inspection recipe

Various systems and methods for creating persistent data for a wafer and using persistent data for inspection-related functions are provided. One system includes a set of processor nodes coupled to a detector of an inspection system. Each of the processor nodes is configured to receive a portion of image data generated by the detector during scanning of a wafer. The system also includes an array of storage media separately coupled to each of the processor nodes. The processor nodes are configured to send all of the image data or a selected portion of the image data received by the processor nodes to the arrays of storage media such that all of the image data or the selected portion of the image data generated by the detector during the scanning of the wafer is stored in the arrays of the storage media.

Systems and methods for creating persistent data for a wafer and for using persistent data for inspection-related functions

Computer-implemented methods, computer-readable media, and systems for identifying one or more optical modes of an inspection system as candidates for use in inspection of a layer of a wafer are provided. One method includes determining one or more characteristics of images of the layer of the wafer acquired using the inspection system and different optical modes available on the inspection system. The method also includes identifying a first portion of the different optical modes as not candidates for use in the inspection of the layer of the wafer based on the one or more characteristics of the images. In addition, the method includes generating output by eliminating the first portion of the different optical modes from the different optical modes at which the images were acquired such that the output includes a second portion of the different optical modes indicated as the candidates for use in the inspection.

Computer-implemented methods, computer-readable media, and systems for identifying one or more optical modes of an inspection system as candidates for use in inspection of a layer of a wafer

Methods and systems for generating information to be used for selecting values for parameter(s) of a detection algorithm are provided. One method includes without user intervention performing a scan of an area of a wafer using an inspection system and default values for parameter(s) of a detection algorithm to detect defects on the wafer. The method also includes selecting a portion of the defects from results of the scan based on a predetermined maximum number of total defects to be used for selecting values for the parameter(s) of the detection algorithm. The method further includes storing information, which includes values for the parameter(s) of the detection algorithm determined for the defects in the portion. The information can be used to select the values for the parameter(s) of the detection algorithm to be used for the inspection recipe without performing an additional scan of the wafer subsequent to the scan.

Methods and systems for generating information to be used for selecting values for one or more parameters of a detection algorithm

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Campbell, CA, United States of America

Chris Maher