Kla-Tencor Technologies Corporation

Kla Corporation

Kla Tencor Corporation

Sandeep Bhagwat

Martin Plihal

Patrick Huet

Ardis Liang

Peter Eldredge

Maruti Shanbhag

Robinson Piramuthu

Lisheng Gao

Ashok V Kulkarni

Kenong Wu

Saravanan Paramasivam

Tong Huang

Sharon McCauley

Cecelia Anne Campochiaro

Michal Kowalski

David Winslow Randall

Niveditha Lakshmi Narasimhan

Jianxin Zhang

Vivekanand Kini

Ganesh Meenakshisundaram

Ariel Tribble

Shaio Roan

Kai Liu

Kai Chi

Alan Davila

Karthik Purushothaman

Po-Shou Liao

Surya Vanamali

Sravani Desu

Charles Lai

Jimmy Liao

Bing Li

Marcus Liesching

Ilya Languev

Leslie F Smith

Felix Lai

Suresh Selvaraj

Benny Huang

Sino Ho

Ellis E-Li Chang

Anthony Cheung

Michael J Bellon

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

Hybrid methods for classifying defects in semiconductor manufacturing are provided. The methods include applying a flexible sequence of rules for defects to inspection data. The sequence of rules includes deterministic rules, statistical rules, hybrid rules, or some combination thereof. The rules included in the sequence may be selected by a user using a graphical interface. The method also includes classifying the defects based on results of applying the sequence of rules to the inspection data.

Flexible hybrid defect classification for semiconductor manufacturing

An extensible data analysis system for analyzing integrated circuit fabrication data produced during integrated circuit fabrication, including an application tier that selectively runs analysis nodes. The application tier has an architecture for optionally including and excluding a desired selection of the analysis nodes. The application tier architecture allows the selection of the analysis nodes to be dynamically added by a user. A data access tier selectively runs data reader nodes. The data access tier has an architecture for optionally including and excluding a desired selection of the data reader nodes. The data reader nodes interpret a desired variety of data source files containing the integrated circuit fabrication data having different formats for access by the application tier. The data access tier architecture allows the selection of the data reader nodes to be dynamically added by the user.

Data analysis flow engine

A system for determining an assigned classification for a set of physical events on a substrate. Sensors sense the physical events on the substrate and produce event data. A plug in rule module manager receives and manages any number of plug in rule modules. Each plug in rule module has an input, a local filter, an analyzer, and an output. The input receives the event data and confidence values from preceding plug in rule modules. The local filter analyzes the received confidence values from the preceding plug in rule modules and selectively by passes the plug in rule module based at least in part upon the received confidence values from the preceding plug in rule modules. The analyzer analyzes the event data in view of a given classification associated with the plug in rule module, and assigns a confidence value based at least in part upon how well the event data fits the given classification. The output provides the confidence value to subsequent plug in rule modules. A post processor receives the confidence values provided by the plug in rule modules, and makes a final selection of the assigned classification to be associated with the set of physical events from the given classifications based at least in part upon a comparison of the confidence values produced by all plug in rule modules.

Spatial signature analysis

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Milpitas, CA, United States of America

Sandeep Bhagwat