Kla Tencor Corporation

Kla-tencor Technologies Corporation

Kla Corporation

Kla Instruments Corporation

Ashok V Kulkarni

Saibal Banerjee

Brian Duffy

Lisheng Gao

Kris Bhaskar

Santosh Bhattacharyya

Kenong Wu

Eugene Shifrin

Chien-Huei (Adam) Chen

Chien-Huei Adam Chen

Eliezer Rosengaus

Mohan Mahadevan

Gordon Rouse

Songnian Rong

Cecelia Anne Campochiaro

Michal Kowalski

David Winslow Randall

Kais Maayah

Ariel Tribble

Richard Shiflett

Steve Zhou

Manoj Hardikar

Allen Park

Scott Allen Young

Ajay Gupta

Prasanti Uppaluri

Jason Kirkwood

Ingrid B Peterson

Chetana Bhaskar

Jagdish Chandra Saraswatula

Ramon Ynzunza

Rajesh Manepalli

John Kirkland

Govind Thattaisundaram

Kourosh Nafisi

Bjorn Brauer

Richard Wallingford

Jason Z Lin

Hong Chen

Grace Hsiu-Ling Chen

Jing Zhang

Yong Zhang

Junqing Huang

Brian C Leslie

Patrick Huet

Laurent Karsenti

Wei Zhao

Tong Huang

Chris Maher

Sharon McCauley

Aneesh Khullar

John Raymond Jordan, Iii

Sandeep Bhagwat

Michael Lennek

Ping Gu

Jianxin Zhang

Graham Michael Lynch

Vivekanand Kini

Rohit G Israni

Lalita A Balasubramanian

James Quigley

Shaoyu Lu

Maruti Shanbhag

Paul Rockwell

Chwen-Jiann Fang

Ernesto Escorcia

Alpa Kohli

Evgeni Shifrin

Vivek Bhagat

Qiang Song

Lalita Balasubramanian

James A Quigley

A method includes receiving one or more sets of wafer data, identifying one or more primitives from one or more shapes in one or more layers in the one or more sets of wafer data, classifying each of the one or more primitives as a particular primitive type, identifying one or more primitive characteristics for each of the one or more primitives, generating a primitive database of the one or more primitives, generating one or more rules based on the primitive database, receiving one or more sets of design data, applying the one or more rules to the one or more sets of design data to identify one or more critical areas, and generating one or more wafer inspection recipes including the one or more critical areas for an inspection sub-system.

System and method for generation of wafer inspection critical areas

Techniques are provided that can select defects based on criticality of design pattern as well as defect attributes for process window qualification (PWQ). Defects are sorted into categories based on process conditions and similarity of design. Shape based grouping can be performed on the random defects. Highest design based grouping scores can be assigned to the bins, which are then sorted. Particular defects can be selected from the bins. These defects may be reviewed.

Criticality analysis augmented process window qualification sampling

Methods and systems for determining characteristic(s) of patterns of interest (POIs) are provided. One system is configured to acquire output of an inspection system generated at the POI instances without detecting defects at the POI instances. The output is then used to generate a selection of the POI instances. The system then acquires output from an output acquisition subsystem for the selected POI instances. The system also determines characteristic(s) of the POI using the output acquired from the output acquisition subsystem.

Determining one or more characteristics of a pattern of interest on a specimen

Methods and systems for detecting anomalies in images of a specimen are provided. One system includes one or more computer subsystems configured for acquiring images generated of a specimen by an imaging subsystem. The computer subsystem(s) are also configured for determining one or more characteristics of the acquired images. In addition, the computer subsystem(s) are configured for identifying anomalies in the images based on the one or more determined characteristics without applying a defect detection algorithm to the images or the one or more characteristics of the images.

Image based specimen process control

Criticality of a detected defect can be determined based on context codes. The context codes can be generated for a region, each of which may be part of a die. Noise levels can be used to group context codes. The context codes can be used to automatically classify a range of design contexts present on a die without needing certain information a priori.

Defect sensitivity of semiconductor wafer inspectors using design data with wafer image data

Noise induced by pattern-of-interest (POI) image registration and POI vicinity design patterns in intra-die inspection is reduced. POI are grouped into alignment groups by co-occurrence of proximate registration targets. The alignment groups are registered using the co-occurrence of proximate registration targets. Registration by voting is performed, which can measure a degree that each of the patterns-of-interest is an outlier. POI are grouped into at least one vicinity group with same vicinity design effects.

Reducing registration and design vicinity induced noise for intra-die inspection

Methods and systems for generating simulated output for a specimen are provided. One method includes acquiring information for a specimen with one or more computer systems. The information includes at least one of an actual optical image of the specimen, an actual electron beam image of the specimen, and design data for the specimen. The method also includes inputting the information for the specimen into a learning based model. The learning based model is included in one or more components executed by the one or more computer systems. The learning based model is configured for mapping a triangular relationship between optical images, electron beam images, and design data, and the learning based model applies the triangular relationship to the input to thereby generate simulated images for the specimen.

Generating simulated output for a specimen

Shape primitives are used for inspection of a semiconductor wafer or other workpiece. The shape primitives can define local topological and geometric properties of a design. One or more rules are applied to the shape primitives. The rules can indicate presence of a defect or the likelihood of a defect being present. A rule execution engine can search for an occurrence of the shape primitives covered by the at least one rule.

Shape based grouping

Methods and systems for identifying outliers in multiple instances of a pattern of interest (POI) are provided. One system includes one or more computer subsystems configured for acquiring images generated by an imaging subsystem at multiple instances of a POI within a die formed on the specimen. The multiple instances include two or more instances that are located at aperiodic locations within the die. The computer subsystem(s) are also configured for determining a feature of each of the images generated at the multiple instances of the POI. In addition, the computer subsystem(s) are configured for identifying one or more outliers in the multiple instances of the POI based on the determined features.

Outlier detection on pattern of interest image populations

Methods and systems for determining a position of inspection data with respect to a stored high resolution die image are provided. One method includes aligning data acquired by an inspection system for alignment sites on a wafer with data for predetermined alignment sites. The predetermined alignment sites have a predetermined position in die image space of a stored high resolution die image for the wafer. The method also includes determining positions of the alignment sites in the die image space based on the predetermined positions of the predetermined alignment sites in the die image space. In addition, the method includes determining a position of inspection data acquired for the wafer by the inspection system in the die image space based on the positions of the alignment sites in the die image space.

Using high resolution full die image data for inspection

Various embodiments for determining parameters for wafer inspection and/or metrology are provided.

Data perturbation for wafer inspection or metrology setup using a model of a difference

Systems and methods for detecting defects on a wafer are provided. One method includes determining locations of all instances of a weak geometry in a design for a wafer. The locations include random, aperiodic locations. The weak geometry includes one or more features that are more prone to defects than other features in the design. The method also includes scanning the wafer with a wafer inspection system to thereby generate output for the wafer with one or more detectors of the wafer inspection system. In addition, the method includes detecting defects in at least one instance of the weak geometry based on the output generated at two or more instances of the weak geometry in a single die on the wafer.

Detecting defects on a wafer

Systems and methods for generating information for use in a wafer inspection process are provided. One method includes acquiring output of an inspection system for die(s) located on wafer(s), combining the output for the die(s) based on within die positions of the output, determining, on a within die position basis, a statistical property of variation in values of characteristic(s) of the combined output, and assigning the within die positions to different groups based on the statistical properties determined for the within die positions. The method also includes storing information for the within die positions and the different groups to which the within die positions are assigned in a storage medium that is accessible to the inspection system for performing the wafer inspection process, which includes applying defect detection parameter(s) to additional output of the inspection system generated for a wafer based on the information thereby detecting defects on the wafer.

Inspection recipe setup from reference image variation

Systems and methods for determining one or more parameters of a wafer inspection process are provided. One method includes aligning optical image(s) of an alignment target to their corresponding electron beam images generated by an electron beam defect review system. The method also includes determining different local coordinate transformations for different subsets of alignment targets based on results of the aligning. In addition, the method includes determining positions of defects in wafer inspection system coordinates based on coordinates of the defects determined by the electron beam defect review system and the different local coordinate transformations corresponding to different groups of the defects into which the defects have been separated. The method further includes determining one or more parameters for an inspection process for the wafer based on defect images acquired at the determined positions by a wafer inspection system.

Tuning wafer inspection recipes using precise defect locations

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San Jose, CA, United States of America

Ashok V Kulkarni