Timbre Technologies, Inc.

Cadence Design Systems, Inc.

Tokyo Electron Limited

Emmanuel Drege

Junwei Bao

Nickhil Jakatdar

Srinivas Rao Doddi

Vi Vuong

Kevin Chan

Francis Raquel

Svetlana Litvintseva

Mark A Miller

Lawrence Lane

Sanjay K Yedur

Xinhui Niu

Kelly Barry

Shifang Li

Taber H Smith

Joerg Bischoff

Wen Jin

Michael Laughery

Mike Laughery

Ron Pyke

Jin Wen

Brian Lee

Doris Chin

Shifang Ll

A method and apparatus for modeling and cross correlation of design predicted criticalities include a feedback loop where information from the manufacturing process is provided to cross correlation engine for optimization of semiconductor manufacturing. The information may include parametric information, functional information, and hot spots determination. The sharing of information allows for design intent to be reflected in manufacturing metrology space; thus, allowing for more intelligent metrology and reduces cycle time.

Modeling and cross correlation of design predicted criticalities for optimization of semiconductor manufacturing

Disclosed are a method, a system, and a computer program product for implementing hotspot detection, repair, and optimization of an electronic circuit design, which, in some embodiments, defines, identifies criteria for hotspots/metrics or optimization objective function; performs the initial hotspot or metric prediction; identifies correction candidate(s); applies a correction candidate to the electronic circuit design; and determines whether the outcome of applying the correction candidate is acceptable. The method or the system identifies custom correction candidate(s) or custom command(s) and identifies one or more hints for the predicted hotspots or metrics; provides a single architecture to use a first model for hotspot identification/correction and a second model for design check; and provides the capability to apply a correction for a hotspot or metric, evaluate the effectiveness of the correction on the fly, and revert any changes made to the electronic circuit design by the correction.

Methods, systems, and computer program product for implementing hotspot detection, repair, and optimization of an electronic circuit design

A method and apparatus for mask optimization is provided. Mask design and production is optimized by providing proper weighting parameters for critical features. The parameters may include information such as parametric information, functional information, and hot spots determination.

Method and system for mask optimization

A method and apparatus for process optimization is provided. Process optimization improves parametric and functional yield post mask manufacturing.

Method and system for process optimization

A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured using a metrology device. A second diffraction signal is generated using a machine learning system, where the machine learning system receives as an input one or more parameters that characterize a profile of the structure to generate the second diffraction signal. The first and second diffraction signals are compared. When the first and second diffraction signals match within a matching criterion, a feature of the structure is determined based on the one or more parameters or the profile used by the machine learning system to generate the second diffraction signal.

Optical metrology of structures formed on semiconductor wafers using machine learning systems

A method and apparatus for inspection optimization is provided. Inspection optimization improves the parametric and functional yield using optimized inspection lists for in-line semiconductor manufacturing metrology and inspection equipment.

Method and system for inspection optimization in design and production of integrated circuits

The optimization of an optical metrology model for use in measuring a wafer structure is evaluated. An optical metrology model having metrology model variables, which includes profile model parameters of a profile model, is developed. One or more goals for metrology model optimization are selected. One or more profile model parameters to be used in evaluating the one or more selected goals are selected. One or more metrology model variables to be set to fixed values are selected. One or more selected metrology model variables are set to fixed values. One or more termination criteria for the one or more selected goals are set. The optical metrology model is optimized using the fixed values for the one or more selected metrology model variables. Measurements for the one or more selected profile model parameters are obtained using the optimized optical metrology model. A determination is then made as to whether the one or more termination criteria are met by the obtained measurements.

Optical metrology model optimization based on goals

A profile model can be selected for use in examining a structure formed on a semiconductor wafer using optical metrology by obtaining an initial profile model having a set of profile parameters. A machine learning system is trained using the initial profile model. A simulated diffraction signal is generated for an optimized profile model using the trained machine learning system, where the optimized profile model has a set of profile parameters with the same or fewer profile parameters than the initial profile model. A determination is made as to whether the one or more termination criteria are met. If the one or more termination criteria are met, the optimized profile model is modified and another simulated diffraction signal is generated using the same trained machine learning system.

Selecting a profile model for use in optical metrology using a machine learning system

A profile model for use in optical metrology of structures in a wafer is selected, the profile model having a set of geometric parameters associated with the dimensions of the structure. The set of geometric parameters is selected to a set of optimization parameters. The number of optimization parameters within the set of optimization parameters is less than the number of geometric parameters within the set of geometric parameters. A set of selected optimization parameters is selected from the set of optimization parameters. The parameters of the set of selected geometric parameters are used as parameters of the selected profile model. The selected profile model is tested against one or more termination criteria.

Model and parameter selection for optical metrology

Specific wavelengths to use in optical metrology of an integrated circuit can be selected using one or more selection criteria and termination criteria. Wavelengths are selected using the selection criteria, and the selection of wavelengths is iterated until the termination criteria are met.

Selection of wavelengths for integrated circuit optical metrology

A hypothetical profile is used to model the profile of a structure formed on a semiconductor wafer to use in determining the profile of the structure using optical metrology. To select a hypothetical profile, sample diffraction signals are obtained from measured diffraction signals of structures formed on the wafer, where the sample diffraction signals are a representative sampling of the measured diffraction signals. A hypothetical profile is defined and evaluated using a sample diffraction signal from the obtained sample diffraction signals.

Selecting a hypothetical profile to use in optical metrology

A profile model for use in optical metrology of structures in a wafer is selected, the profile model having a set of geometric parameters associated with the dimensions of the structure. A set of optimization parameters is selected for the profile model using one or more input diffraction signals and one or more parameter selection criteria. The selected profile model and the set of optimization parameters are tested against one or more termination criteria. The process of selecting a profile model, selecting a set of optimization parameters, and testing the selected profile model and set of optimization parameters is performed until the one or more termination criteria are met.

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San Jose, CA, United States of America

Emmanuel Drege