Kla Tencor Corporation

Ankit Jain

Martin Plihal

Saravanan Paramasivam

Prasanti Uppaluri

Erfan Soltanmohammadi

Sairam Ravu

Kenong Wu

Allen Park

Gordon Rouse

Thirupurasundari Jayaraman

Michael Lennek

Raghavan Konuru

Srikanth Kandukuri

Vijay Ramachandran

Aravindh Balaji

Sarath Shekkizhar

Anil Raman

Praveen Gunasekaran

Wafer inspection with stable nuisance rates and defect of interest capture rates are disclosed. This technique can be used for discovery of newly appearing defects that occur during the manufacturing process. Based on a first wafer, defects of interest are identified based on the classified filtered inspection results. For each remaining wafer, the defect classifier is updated and defects of interest in the next wafer are identified based on the classified filtered inspection results.

Diagnostic methods for the classifiers and the defects captured by optical tools

A system, method, and non-transitory computer readable medium are provided for training and applying defect classifiers in wafers having deeply stacked layers. In use, a plurality of images generated by an inspection system for a location of a defect detected on a wafer by the inspection system are acquired. The location on the wafer is comprised of a plurality of stacked layers, and each image of the plurality of images is generated by the inspection system at the location using a different focus setting. Further, a classification of the defect is determined, utilizing the plurality of images.

System, method for training and applying defect classifiers in wafers having deeply stacked layers

Defects can be identified using a hybrid design layout that includes a printable layer and a non-printed layer. The hybrid design layout can be generated by incorporating at least a portion of the non-printable layer layout with the printable layer layout. Defects can be identified using optical or scanning electron beam images.

Hybrid design layout to identify optical proximity correction-related systematic defects

Methods and systems for selecting a mode for inspection of a specimen are provided. One method includes determining how separable defects of interest (DOIs) and nuisances detected on a specimen are in one or more modes of an inspection subsystem. The separability of the modes for the Dais and nuisances is used to select a subset of the modes for inspection of other specimens of the same type. Other characteristics of the performance of the modes may be used in combination with the separability to select the modes. The subset of modes selected based on the separability may also be an initial subset of modes for which additional analysis is performed to determine the final subset of the modes.

Mode selection for inspection

Systems and methods are disclosed for storing dynamic layer content in a design file. A design file is received having design data corresponding to a plurality of process layers. A geometric operation formula is also received. A processor generates a polygon having dynamic layer content that is formed by applying the geometric operation formula on two or more of the plurality of process layers. The updated design file is stored, the design file now having a polygon having dynamic layer content.

Methods to store dynamic layer content inside a design file

Methods and systems for generating defect samples are provided. One method includes identifying a set of defects detected on a wafer having the most diversity in values of at least one defect attribute and generating different tiles for different defects in the set. The tiles define a portion of all values for the at least one attribute of all defects detected on the wafer that are closer to the values for the at least one attribute of their corresponding defects than the values for the at least one attribute of other defects. In addition, the method includes separating the defects on the wafer into sample bins corresponding to the different tiles based on their values of the at least one attribute, randomly selecting defect(s) from each of two or more of the sample bins, and creating a defect sample for the wafer that includes the randomly selected defects.

Production sample shaping that preserves re-normalizability

Methods and systems for training an inspection-related algorithm are provided. One system includes one or more computer subsystems configured for performing an initial training of an inspection-related algorithm with a labeled set of defects thereby generating an initial version of the inspection-related algorithm and applying the initial version of the inspection-related algorithm to an unlabeled set of defects. The computer subsystem(s) are also configured for altering the labeled set of defects based on results of the applying. The computer subsystem(s) may then iteratively re-train the inspection-related algorithm and alter the labeled set of defects until one or more differences between results produced by a most recent version and a previous version of the algorithm meet one or more criteria. When the one or more differences meet the one or more criteria, the most recent version of the inspection-related algorithm is outputted as the trained algorithm.

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Ballston Spa, NY, United States of America

Ankit Jain