Kla-tencor Technologies Corporation

Kla Corporation

Mike Von Den Hoff

John Charles Robinson

David W Price

Oreste Donzella

Robert J Rathert

Ingrid B Peterson

Barry Saville

Thomas Groos

Shifang Li

Stilian Pandev

Chet V Lenox

Kara L Sherman

Robert Cappel

Justin Lach

Naema Bhatti

Teng-Song Lim

Doug Sutherland

Jim Wiley

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

Methods that include acquiring aerial images of a reticle for different values of a member of a set of lithographic variables are provided. One method also includes determining a presence of an anomaly in a design pattern of the reticle by comparing at least one pair of the aerial images corresponding to at least two of the different values. A different method includes comparing at least one pair of the aerial images corresponding to at least two of the different values and determining an area on the reticle where a lithography process using the reticle is most susceptible to failure based on the results of the comparison. Another embodiment includes determining a presence of transient repeating defects on the reticle by subtracting non-transient defects from the aerial images and comparing at least one pair of the aerial images corresponding to at least two of the different values.

Qualifying patterns, patterning processes, or patterning apparatus in the fabrication of microlithographic patterns

The invention is a method of determining the presence of an anomaly in qualifying a pattern, patterning process, or patterning apparatus used in the fabrication of microlithographic patterns. A preferred implementation of the method qualifies incoming reticles and process conditions on test wafers to maximize the available usable process window for a given reticle exposure tool combination. Practicing this method on test wafers enables the identification of spatial areas where a process will fail first and candidate regions for carrying out defect inspection and metrology. Other preferred implementations of the method qualify masks, reticles, or other patterns characterized by data bases on which are stored image data acquired by practice of aerial image measurement system (AIMS) or design rule checking (DRC) techniques.

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Munich, Germany

Mike Von Den Hoff