Abstract: An initial probability of occurrence of a stochastic defect over an inspection area of a workpiece is received. All locations of the stochastic defects are sorted by the initial probability of occurrence. A cumulative expected defect count is determined and the cumulative expected defect count is normalized to be a fraction of a total expected defect count. A number of defect locations is determined to capture potential stochastic defects above a threshold of total stochastic defects.

Abstract: Using an initial probability of occurrence of a stochastic defect over an inspection area of a workpiece, one or more defects within the inspection area are imaged using an optical tool or an electron beam tool. A probability of occurrence of a stochastic defect at each of the defect locations is generated using the model. The defect locations are grouped into probability bins. A consistency between the initial probability and observed results is determined and the model can be tuned based on the consistency.

Abstract: Based on an initial probability of occurrence of a stochastic defect over a layout of a workpiece, a subset of locations on the workpiece are selected where the initial probability is above a threshold. The subset of locations are grouped by pattern shapes. An expected defect count is determined for each of the pattern shapes. A subset of the pattern shapes is then selected for repair.

Abstract: A system for mask design repair may develop a simulation-based model of a layer thickness after one or more process steps for fabricating features on a sample, develop a transformed model of the fabrication process that emulates the simulation-based model and has a faster evaluation speed than the simulation-based model, and where the inputs to the transformed model include the input mask design, and where the outputs of the transformed model include one or more output parameters associated with fabrication of the input mask design as well as one or more sensitivity metrics describing sensitivities of the one or more output parameters to variations of the input mask design. The system may further receive a candidate mask design and generate a repaired mask design based on the transformed model and the candidate mask design.

Abstract: A system for mask design repair may develop a simulation-based model of a layer thickness after one or more process steps for fabricating features on a sample, develop a transformed model of the fabrication process that emulates the simulation-based model and has a faster evaluation speed than the simulation-based model, and where the inputs to the transformed model include the input mask design, and where the outputs of the transformed model include one or more output parameters associated with fabrication of the input mask design as well as one or more sensitivity metrics describing sensitivities of the one or more output parameters to variations of the input mask design. The system may further receive a candidate mask design and generate a repaired mask design based on the transformed model and the candidate mask design.

Abstract: Systems and methods for prediction and measurement of overlay errors are disclosed. Process-induced overlay errors may be predicted or measured utilizing film force based computational mechanics models. More specifically, information with respect to the distribution of film force is provided to a finite element (FE) model to provide more accurate point-by-point predictions in cases where complex stress patterns are present. Enhanced prediction and measurement of wafer geometry induced overlay errors are also disclosed.

Abstract: A controller is configured to perform at least a first characterization process prior to at least one discrete backside film deposition process on a semiconductor wafer; perform at least an additional characterization process following the at least one discrete backside film deposition process; determine at least one of a film force or one or more in-plane displacements for at least one discrete backside film deposited on the semiconductor wafer via the at least one discrete backside film deposition process based on the at least the first characterization process and the at least the additional characterization process; and provide at least one of the film force or the one or more in-plane displacements to at least one process tool via at least one of a feed forward loop or a feedback loop to improve performance of one or more fabrication processes.

Abstract: A mask pattern for a semiconductor device can be used as an input to determine a photoresist thickness probability distribution using a machine learning module. For example, the machine learning module can determine a probability map of Z-height. This can be used to determine stochastic variation in photoresist thickness for a semiconductor device. The Z-height may be calculated at a coordinate in the X-direction and Y-direction.

Abstract: A controller is configured to perform at least a first characterization process prior to at least one discrete backside film deposition process on a semiconductor wafer; perform at least an additional characterization process following the at least one discrete backside film deposition process; determine at least one of a film force or one or more in-plane displacements for at least one discrete backside film deposited on the semiconductor wafer via the at least one discrete backside film deposition process based on the at least the first characterization process and the at least the additional characterization process; and provide at least one of the film force or the one or more in-plane displacements to at least one process tool via at least one of a feed forward loop or a feedback loop to improve performance of one or more fabrication processes.

Abstract: A controller is configured to perform at least a first characterization process prior to at least one discrete backside film deposition process on a semiconductor wafer; perform at least an additional characterization process following the at least one discrete backside film deposition process; determine at least one of a film force or one or more in-plane displacements for at least one discrete backside film deposited on the semiconductor wafer via the at least one discrete backside film deposition process based on the at least the first characterization process and the at least the additional characterization process; and provide at least one of the film force or the one or more in-plane displacements to at least one process tool via at least one of a feed forward loop or a feedback loop to improve performance of one or more fabrication processes.

Abstract: Prediction based systems and methods for optimizing wafer chucking and lithography control are disclosed. Distortions predicted to occur when a wafer is chucked by a chucking device are calculated and are utilized to control chucking parameters of the chucking device. Chucking parameters may include chucking pressures and chucking sequences. In addition, predicted distortions may also be utilized to facilitate application of anticipatory corrections. Controlling chucking parameters and/or applying anticipatory corrections help reducing or minimizing overlay errors.

Abstract: Wafer geometry measurement tools and methods for providing improved wafer geometry measurements are disclosed. Wafer front side, backside and flatness measurements are taken into consideration for semiconductor process control. The measurement tools and methods in accordance with embodiments of the present disclosure are suitable for handling any types of wafers, including patterned wafers, without the shortcomings of conventional metrology systems.

Abstract: Systems and methods for prediction and measurement of overlay errors are disclosed. Process-induced overlay errors may be predicted or measured utilizing film force based computational mechanics models. More specifically, information with respect to the distribution of film force is provided to a finite element (FE) model to provide more accurate point-by-point predictions in cases where complex stress patterns are present. Enhanced prediction and measurement of wafer geometry induced overlay errors are also disclosed.

Abstract: A controller is configured to perform at least a first characterization process prior to at least one discrete backside film deposition process on a semiconductor wafer; perform at least an additional characterization process following the at least one discrete backside film deposition process; determine at least one of a film force or one or more in-plane displacements for at least one discrete backside film deposited on the semiconductor wafer via the at least one discrete backside film deposition process based on the at least the first characterization process and the at least the additional characterization process; and provide at least one of the film force or the one or more in-plane displacements to at least one process tool via at least one of a feed forward loop or a feedback loop to improve performance of one or more fabrication processes.

Abstract: Systems and methods for improving results of wafer higher order shape (HOS) characterization and wafer classification are disclosed. The systems and methods in accordance with the present disclosure are based on localized shapes. A wafer map is partitioned into a plurality of measurement sites to improve the completeness of wafer shape representation. Various site based HOS metric values may be calculated for wafer characterization and/or classification purposes, and may also be utilized as control input for a downstream application. In addition, polar grid partitioning schemes are provided. Such polar grid partitioning schemes may be utilized to partition a wafer surface into measurement sites having uniform site areas while providing good wafer edge region coverage.

Abstract: Systems and methods for prediction and measurement of overlay errors are disclosed. Process-induced overlay errors may be predicted or measured utilizing film force based computational mechanics models. More specifically, information with respect to the distribution of film force is provided to a finite element (FE) model to provide more accurate point-by-point predictions in cases where complex stress patterns are present. Enhanced prediction and measurement of wafer geometry induced overlay errors are also disclosed.

Abstract: Systems and methods for improving results of wafer higher order shape (HOS) characterization and wafer classification are disclosed. The systems and methods in accordance with the present disclosure are based on localized shapes. A wafer map is partitioned into a plurality of measurement sites to improve the completeness of wafer shape representation. Various site based HOS metric values may be calculated for wafer characterization and/or classification purposes, and may also be utilized as control input for a downstream application. In addition, polar grid partitioning schemes are provided. Such polar grid partitioning schemes may be utilized to partition a wafer surface into measurement sites having uniform site areas while providing good wafer edge region coverage.

Abstract: The present invention may include acquiring a wafer shape value at a plurality of points of a wafer surface at a first and second process level, generating a wafer shape change value at each of the points, generating a set of slope of shape change values at each of the points, calculating a set of process tool correctables utilizing the generated set of slope of shape change values, generating a set of slope shape change residuals (SSCRs) by calculating a slope of shape change residual value at each of the points utilizing the set of process tool correctables, defining a plurality of metric analysis regions distributed across the surface, and then generating one or more residual slope shape change metrics for each metric analysis region based on one or more SSCRs within each metric analysis region.

Abstract: Prediction based systems and methods for optimizing wafer chucking and lithography control are disclosed. Distortions predicted to occur when a wafer is chucked by a chucking device are calculated and are utilized to control chucking parameters of the chucking device. Chucking parameters may include chucking pressures and chucking sequences. In addition, predicted distortions may also be utilized to facilitate application of anticipatory corrections. Controlling chucking parameters and/or applying anticipatory corrections help reducing or minimizing overlay errors.

Abstract: Prediction based systems and methods for optimizing wafer chucking and lithography control are disclosed. Distortions predicted to occur when a wafer is chucked by a chucking device are calculated and are utilized to control chucking parameters of the chucking device. Chucking parameters may include chucking pressures and chucking sequences. In addition, predicted distortions may also be utilized to facilitate application of anticipatory corrections. Controlling chucking parameters and/or applying anticipatory corrections help reducing or minimizing overlay errors.