Kla Tencor Corporation

Kla-tencor Technologies Corporation

Patrick Huet

Martin Plihal

Robinson Piramuthu

Yan Xiong

Cho Huak Teh

Christopher W Lee

George J Kren

Eliezer Rosengaus

David Y Wang

Tong Huang

Paul J Sullivan

Chris Maher

Sharon McCauley

Sandeep Bhagwat

Maruti Shanbhag

Lisheng Gao

Ashok V Kulkarni

Brian Duffy

Kenong Wu

Saravanan Paramasivam

Chien-Huei (Adam) Chen

John Raymond Jordan, Iii

Chris W Lee

Cecelia Anne Campochiaro

David Winslow Randall

Michal Kowalski

Thomas Trautzsch

Jianxin Zhang

Ariel Tribble

Mike Van Riet

Vivekanand Kini

Stewart Hill

Tai-Kam Ng

Peter Eldredge

Shaio Roan

Kai Liu

Mark Dishner

Kai Chi

Luc Debarge

Adam Chien-Huei Chen

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

Computer-implemented methods for performing one or more defect-related functions are provided. One method for identifying noise in inspection data includes identifying events detected in a number of sets of inspection data that is less than a predetermined number as noise. One method for binning defects includes binning the defects into groups based on defect characteristics and the sets of the inspection data in which the defects were detected. One method for selecting defects for defect analysis includes binning defects into group(s) based on proximity of the defects to each other and spatial signatures formed by the group(s). A different method for selecting defects for defect analysis includes selecting defects having the greatest diversity of defect characteristic(s) for defect analysis. One method includes classifying defects on a specimen using inspection data generated for the specimen combined with defect review data generated for the specimen.

Computer-implemented methods for performing one or more defect-related functions

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

A method for analyzing defect information on a substrate, including logically dividing the substrate into zones, and detecting defects on the substrate to produce the defect information. The defect information from the substrate is analyzed on a zone by zone basis to produce defect level classifications for the defects within each zone. The zonal defect level classifications are analyzed according to at least one analysis method. The defect level classifications are preferably selected from a group of defect level classifications that is specified by a recipe. Preferably, the at least one analysis method includes at least one of zonal defect distribution, automatic defect classification, spatial signature analysis, and excursion detection. The defect level classifications preferably include at least one of individual defect, defect cluster, and spatial signature analysis signature. In one embodiment the defect information is logically divided into configurable zones after the defects on the substrate have been detected.

Process excursion detection

A computerized method for categorizing defects on a substrate. A list of defects on the substrate is received as input to a processor, where each defect is represented by a defect location and an associated micro-defect code. The input is analyzed with the processor to detect spatial clusters of defects on the substrate. The spatial clusters are analyzed with the processor to determine which of the spatial clusters represent known macro-defects and which of the spatial clusters represent unknown macro-defects. The micro-defect code associated with each defect that is included in one of the spatial clusters that is determined to be a known macro-defect is changed with the processor with a macro-defect code that is associated solely with the known macro-defect. The processor analyzes the defects that are included in one of the spatial clusters that is determined to be an unknown macro-defect to determine a predominantly occurring micro-defect code. The processor changes the micro-defect code associated with each defect that is included in the one spatial cluster that is determined to be an unknown macro-defect with the predominantly-occurring micro-defect code. The processor sends the changed list of defects on the substrate as output.

Multi-scale classification of defects

Methods and systems for generating an inspection process for an inspection system are provided. One computer implemented method includes generating inspection data for a selected defect on a specimen at different values of one or more image acquisition parameters of the inspection system. The method also includes determining which of the different values produces the best inspection data for the selected defect. In addition, the method includes selecting the different values determined to produce the best inspection data as values of the one or more image acquisition parameters to be used for the inspection process.

Methods and systems for generating an inspection process for an inspection system

Wafer inspection systems and methods are provided. One inspection system includes a module measurement cell coupled to a host inspection system by a wafer handler. The module measurement cell is configured to inspect a wafer using one or more modes prior to inspection of the wafer by the host inspection system. The one or more modes include backside inspection, edge inspection, frontside macro defect inspection, or a combination thereof. Another inspection system includes two or more low resolution electronic sensors arranged at multiple viewing angles. The sensors are configured to detect light returned from a wafer substantially simultaneously. A method for analyzing inspection data includes selecting a template corresponding to a support device that contacts a backside of a wafer prior to inspection of the backside of the wafer. The method also includes subtracting data representing the template from inspection data generated by inspection of the backside of the wafer.

Wafer inspection systems and methods for analyzing inspection data

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

A method for analyzing defects on a substrate, including inspecting the substrate to detect the defects, identifying the defects by location, analyzing the defects to detect extended objects, and analyzing the extended objects for repetition across the substrate. Thus, the present invention extends beyond the present analysis methods, by analyzing the extended objects for repetition across the substrate. In this manner, correlation with processing problems can be more readily detected, in cases where the individual defects themselves, of which the extended objects are formed, do not appear to have repeating properties.

Detection of spatially repeating signatures

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

Growing community of inventors

San Jose, CA, United States of America

Patrick Huet