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: 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: 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: The present disclosure relates to a method, and apparatus for detecting an application freezing problem, and a device and a storage medium. The method comprises: detecting time consumed for a Runloop in a first thread of an application executing a task; when it is detected that the consumed time reaches a preset threshold value, at least acquiring a call stack of the first thread and the current first execution state of the Runloop, and writing the call stack into a preset file; detecting an execution state of the Runloop after the time consumed for the task reaches the preset threshold value; and if the Runloop does not enter a second execution state after the first execution state before the application is closed, sending the preset file to a remote server. By means of the solution provided in the embodiments of the present disclosure, an application freezing problem can be identified and detected.

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.