Kla Tencor Corporation

Kla-tencor Technologies Corporation

Kla-tenor Corp.

Pavel Izikson

John Charles Robinson

Michael E Adel

Mark Ghinovker

Amir Widmann

Joel L Seligson

Mike Adel

Dongsub Choi

Anat Marchelli

Boris Simkin

David Tulipman

Vladimir Levinski

Daniel Kandel

Guy Cohen

Chris Mack

Moshe E Preil

Boris Golovanevsky

Dana Klein

Jorge Poplawski

Elyakim Kassel

Brad Eichelberger

Atsuhiko Kato

John Robinson

Various methods and systems for creating or performing a dynamic sampling scheme for a process during which measurements are performed on wafers are provided. One method for creating a dynamic sampling scheme for a process during which measurements are performed on wafers includes performing the measurements on all of the wafers in at least one lot at all measurement spots on the wafers. The method also includes determining an optimal sampling scheme, an enhanced sampling scheme, a reduced sampling scheme, and thresholds for the dynamic sampling scheme for the process based on results of the measurements. The thresholds correspond to values of the measurements at which the optimal sampling scheme, the enhanced sampling scheme, and the reduced sampling scheme are to be used for the process.

Methods and systems for creating or performing a dynamic sampling scheme for a process during which measurements are performed on wafers

Various methods and systems for creating or performing a dynamic sampling scheme for a process during which measurements are performed on wafers are provided. One method for creating a dynamic sampling scheme for a process during which measurements are performed on wafers includes performing the measurements on all of the wafers in at least one tot at all measurement spots on the wafers. The method also includes determining an optimal sampling scheme, an enhanced sampling scheme, a reduced sampling scheme, and thresholds for the dynamic sampling scheme for the process based on results of the measurements. The thresholds correspond to values of the measurements at which the optimal sampling scheme, the enhanced sampling scheme, and the reduced sampling scheme are to be used for the process.

The present invention may include performing a first measurement on a wafer of a first lot of wafers via an omniscient sampling process, calculating a first set of process tool correctables utilizing one or more results of the measurement performed via an omniscient sampling process, randomly selecting a set of field sampling locations of the wafer of a first lot of wafers, calculating a second set of process tool correctables by applying an interpolation process to the randomly selected set of field sampling locations, wherein the interpolation process utilizes values from the first set of process tool correctables for the randomly selected set of field sampling locations in order to calculate correctables for fields of the wafer of the first lot not included in the set of randomly selected fields, and determining a sub-sampling scheme by comparing the first set of process tool correctables to the second set of correctables.

Method and system for providing process tool correctables using an optimized sampling scheme with smart interpolation

The present invention may include measuring tool induced shift (TIS) on at least one wafer of a lot of wafers via an omniscient sampling process, randomly generating a plurality of sub-sampling schemes, each of the set of randomly generated sub-sampling schemes having the same number of sampled fields, measuring TIS at each location of each of the randomly generated sub-sampling schemes, approximating a set of TIS values for each of the randomly generated sub-sampling schemes utilizing the TIS measurements from each of the randomly generated sub-sampling schemes, wherein each set of TIS values for each of the randomly generated sub-sampling schemes is calculated utilizing an interpolation process configured to approximate a TIS value for each location not included in a randomly generated sub-sampling scheme, and determining a selected sub-sampling scheme by comparing each of the calculated sets of TIS values to the measured TIS of the omniscient sampling process.

Method and system for providing tool induced shift using a sub-sampling scheme

The present invention may include performing a first measurement process on a wafer of a lot of wafers, wherein the first measurement process includes measuring one or more characteristics of a plurality of targets distributed across one or more fields of the wafer, determining a set of process tool correctables for a residual larger than a selected threshold level utilizing a loss function, wherein the loss function is configured to fit a model for one or more process tools, as a function of field position, to one or more of the measured characteristics of the plurality of targets, wherein the set of process tool correctables includes one or more parameters of the model that act to minimize the difference between a norm of the residual and the selected threshold, and utilizing the determined process tool correctables to monitor or adjust one or more processes of the process tools.

Method and system for providing process tool correctables

A method of characterizing a process by selecting the process to characterize, selecting a parameter of the process to characterize, determining values of the parameter to use in a test matrix, specifying an eccentricity for the test matrix, selecting test structures to be created in cells on a substrate, processing the substrate through the process using in each cell the value of the parameter as determined by the eccentric test matrix, measuring a property of the test structures in the cells, and developing a correlation between the parameter and the property.

Substrate matrix to decouple tool and process effects

Apparatus and methods are provided for predicting a plurality of unknown parameter values (e.g. overlay error or critical dimension) using a plurality of known parameter values. In one embodiment, the method involves training a neural network to predict the plurality of parameter values. In other embodiments, the prediction process does not depend on an optical property of a photolithography tool. Such predictions may be used to determine wafer lot disposition.

Apparatus and methods for predicting a semiconductor parameter across an area of a wafer

An overlay method for determining the overlay error of a device structure formed during semiconductor processing is disclosed. The overlay method includes producing calibration data that contains overlay information relating the overlay error of a first target at a first location to the overlay error of a second target at a second location for a given set of process conditions. The overlay method also includes producing production data that contains overlay information associated with a production target formed with the device structure. The overlay method further includes correcting the overlay error of the production target based on the calibration data.

Overlay metrology and control method

A method for optimizing alignment performance in a fleet of exposure systems involves characterizing each exposure system in a fleet of exposure systems to generate a set of distinctive distortion profiles associated with each exposure system. The set of distinctive distortion profiles are stored in a database. A wafer having reference pattern formed thereon is provided for further pattern fabrication and an exposure system is selected from the fleet to fabricate a next layer on the wafer. Linear and higher order parameters of the selected exposure system are adjusted using the distinctive distortion profiles to model the distortion of the reference pattern. Once the exposure system is adjusted, it is used to form a lithographic pattern on the wafer.

Method and system for optimizing alignment performance in a fleet of exposure tools

Disclosed are apparatus and methods for obtaining and analyzing various unique metrics or 'target diagnostics' from one or more semiconductor overlay targets. In one embodiment, an overlay target is measured to obtain one or both of two specific types of target diagnostic information, systematic error metrics and/or random noise metrics. The systematic error metrics generally quantify asymmetries of the overlay target, while the random noise metrics quantify and/or qualify the spatial noise that is proximate to or associated with the overlay target.

Use of overlay diagnostics for enhanced automatic process control

Disclosed are methods and apparatus for analyzing the quality of overlay targets. In one embodiment, a method of extracting data from an overlay target is disclosed. Initially, image information or one or more intensity signals of the overlay target are provided. An overlay error is obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. A systematic error metric is also obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. For example, the systematic error may indicate an asymmetry metric for one or more portions of the overlay target. A noise metric is further obtained from the overlay target by applying a statistical model to the image information or the intensity signal(s) of the overlay target. Noise metric characterizes noise, such as a grainy background, associated with the overlay target. In other embodiments, an overlay and/or stepper analysis procedure is then performed based on the systematic error metric and/or the noise metric, as well as the overlay data.

Growing community of inventors

Haifa, Israel

Pavel Izikson