Kla Tencor Corporation

Kla Corporation

Kla-tencor Technologies Corporation

Ardis Liang

Kris Bhaskar

Mark Armstrong McCord

Martin Plihal

Richard Wallingford

Santosh Bhattacharyya

Hubert Altendorfer

Sandeep Bhagwat

Kais Maayah

Saravanan Paramasivam

Sankar Venkataraman

Prasanti Uppaluri

Sagar A Kekare

Michael John Van Riet

Raghav Babulnath

Thirupurasundari Jayaraman

Niveditha Lakshmi Narasimhan

Srikanth Kandukuri

Ganesh Meenakshisundaram

Marcus Liesching

Alan Davila

Karthik Purushothaman

Surya Vanamali

Suresh Selvaraj

Sravani Desu

Methods and systems for generating information for use in setting up a process performed on a specimen are provided. One method includes clustering dies on a specimen based on colors assigned to the dies responsive to predicted defect densities in the dies determined from measurements performed on the specimen thereby generating initial die clusters. The method also includes analyzing the initial die clusters in location space to determine if any of the initial die clusters contain two or more die clusters. In addition, the method includes designating the initial die clusters that do not contain two or more die clusters and the two or more die clusters contained in any of the initial die clusters as the final die clusters. The method further includes storing information for the final die clusters for use in setting up a process performed on the specimen.

Wafer signature local maxima via clustering for metrology guided inspection

To evaluate a semiconductor-fabrication process, a semiconductor wafer is obtained that includes die grouped into modulation sets. Each modulation set is fabricated using distinct process parameters. The wafer is optically inspected to identify defects. A nuisance filter is trained to classify the defects as DOI or nuisance defects. Based on results of the training, a first, preliminary process window for the wafer is determined and die structures having DOI are identified in a first group of modulation sets bordering the first process window. The trained nuisance filter is applied to the identified defects to determine a second, revised process window for the wafer. A third, further revised process window for the wafer is determined based on SEM images of specified care areas in one or more modulation sets within the second, revised process window. A report is generated that specifies the third process window.

Semiconductor hot-spot and process-window discovery combining optical and electron-beam inspection

A method for automatically classifying one or more defects based on electrical design properties includes receiving one or more images of a selected region of a sample, receiving one or more sets of design data associated with the selected region of the sample, locating one or more defects in the one or more images of the selected region of the sample by comparing the one or more images of the selected region of the sample to the one or more sets of design data, retrieving one or more patterns of interest from the one or more sets of design data corresponding to the one or more defects, and classifying the one or more defects in the one or more images of the selected region of the sample based on one or more annotated electrical design properties included in the one or more patterns of interest.

System and method for defect classification based on electrical design intent

Methods and systems for generating inspection results for a specimen with an adaptive nuisance filter are provided. One method includes selecting a portion of events detected during inspection of a specimen having values for at least one feature of the events that are closer to at least one value of at least one parameter of the nuisance filter than the values for at least one feature of another portion of the events. The method also includes acquiring output of an output acquisition subsystem for the sample of events, classifying the events in the sample based on the acquired output, and determining if one or more parameters of the nuisance filter should be modified based on results of the classifying. The nuisance filter or the modified nuisance filter can then be applied to results of the inspection of the specimen to generate final inspection results for the specimen.

Adaptive nuisance filter

Methods for generating a standard reference die for use in a die to standard reference die inspection and methods for inspecting a wafer are provided. One computer-implemented method for generating a standard reference die for use in a die to standard reference die inspection includes acquiring output of an inspection system for a centrally located die on a wafer and one or more dies located on the wafer. The method also includes combining the output for the centrally located die and the one or more dies based on within die positions of the output. In addition, the method includes generating the standard reference die based on results of the combining step.

Methods for generating a standard reference die for use in a die to standard reference die inspection and methods for inspecting a wafer

Disclosed are methods and apparatus for facilitating procedures implemented on an analysis tool are provided herein. In one embodiment, an apparatus includes an analyzer module arranged for managing an analyzer tool and causing a high resolution image generated by the analyzer tool to be presented in a display. The apparatus also includes an inspector interface module arranged for simulating an inspector interface in the display. The inspector interface includes features that are available on a corresponding inspection tool, and the inspector interface is based at least in part on defect results from the inspection tool. In one embodiment, the analyzer module executes without knowledge of the inspector interface module and visa versa, and the apparatus includes a synchronization mechanism that knows about these two modules and also is capable of communicating with these two modules.

Apparatus and methods for analyzing defects on a sample

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Pleasanton, CA, United States of America

Ardis Liang