Kla Corporation

Kla Tencor Corporation

Ganesh Meenakshisundaram

Ardis Liang

Sandeep Bhagwat

Narayani Narasimhan

Suresh Selvaraj

Surya Vanamali

Marcus Liesching

Sravani Desu

Alan Davila

Alex Teng Song Lim

Karthik Purushothaman

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

Images of semiconductor wafers can be hashed to determine a fixed length hash string for each of the images. Pattern synonyms can be determined from the hash strings. The pattern synonyms can be grouped. A degree of similarity between images in the groups is adjustable via a hamming distance. This can be used for various applications, including determination of latent defects.

Unsupervised pattern synonym detection using image hashing

Embodiments herein include methods, systems, and apparatuses for die screening using inline defect information. Such embodiments may include receiving a plurality of defects, receiving wafersort electrical data for a plurality of dies, classifying each of the defects as a defect-of-interest or nuisance, determining a defect-of-interest confidence for each of the defects-of-interest, determining a die return index for each of the dies containing at least one of the defects-of-interest, determining a die return index cutline, and generating an inking map. Each of the defects may be associated with a die in the plurality of dies. Each of the dies may be tagged as passing a wafersort electrical test or failing the wafersort electrical test. Classifying each of the defects as a defect-of-interest or nuisance may be accomplished using a defect classification model, which may include machine learning. The inking map may be electronically communicated to an inking system.

Die screening using inline defect information

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Austin, TX, United States of America

Ganesh Meenakshisundaram