Kla Tencor Corporation

Kla Corporation

Barry Saville

Allen Park

John Charles Robinson

David W Price

Robert J Rathert

Oreste Donzella

Mike Von Den Hoff

Thomas Groos

Youseung Jin

Sungchan Cho

Shifang Li

Stilian Pandev

Chet V Lenox

Kara L Sherman

Robert Cappel

Justin Lach

Naema Bhatti

Teng-Song Lim

Doug Sutherland

A die screening system may receive die-resolved metrology data for a population of dies on one or more samples from the one or more in-line metrology tools after one or more fabrication steps, where the die-resolved metrology data includes images generated using one or more measurement configurations of the one or more in-line metrology tools. In this way, the die-resolved metrology data provides many measurement channels per die, where a particular measurement channel includes data from a particular pixel of a particular image. The controller may then generate screening data for the population of dies from the die-resolved metrology data, where the screening data includes a subset of the plurality of measurement channels of the die-resolved metrology data, and screen the plurality of dies into two or more disposition classes including at least outlier dies based on variability in the screening data.

Imaging reflectometry for inline screening

An inspection system may include a controller communicatively coupled to one or more in-line sample analysis tools including, but not limited to, an inspection tool or a metrology tool. The controller may identify defects in a population of dies based on data received from at least one of the one or more in-line sample analysis tools, assign weights to the identified defects indicative of predicted impact of the identified defects on reliability of the dies using a weighted defectivity model, generate defectivity scores for the dies in the population by aggregating the weighted defects in the respective dies in the population, and determine a set of outlier dies based on the defectivity scores for the dies in the population, wherein at least some of the set of outlier dies are isolated from the population.

Advanced in-line part average testing

The process for design based assessment includes the following steps. First, the process defines multiple patterns of interest (POIs) utilizing design data of a device and then generates a design based classification database. Further, the process receives one or more inspection results. Then, the process compares the inspection results to each of the plurality of POIs in order to identify occurrences of the POIs in the inspection results. In turn, the process determines yield impact of each POI utilizing process yield data and monitors a frequency of occurrence of each of the POIs and the criticality of the POIs in order to identify process excursions of the device. Finally, the process determines a device risk level by calculating a normalized polygon frequency for the device utilizing a frequency of occurrence for each of the critical polygons and a criticality for each of the critical polygons.

Based device risk assessment

The process for designed based assessment includes the following steps. First, the process defines multiple patterns of interest (POIs) utilizing design data of a device and then generates a design based classification database. Further, the process receives one or more inspection results. Then, the process compares the inspection results to each of the plurality of POIs in order to identify occurrences of the POIs in the inspection results. In turn, the process determines yield impact of each POI utilizing process yield data and monitors a frequency of occurrence of each of the POIs and the criticality of the POIs in order to identify process excursions of the device. Finally, the process determines a device risk level by calculating a normalized polygon frequency for the device utilizing a frequency of occurrence for each of the critical polygons and a criticality for each of the critical polygons.

Growing community of inventors

Gansevoort, United Kingdom

Barry Saville