A method and a system for correcting lithography process hotspots based on stress damping adjustment are provided. The method includes: acquiring a mark hotspot of a mask pattern; forming N annuli centered on the mark hotspot from inner to outer on a mask; moving vertexes of the mask pattern located in each annulus by a specific distance in a direction deviating from the mark hotspot and connecting the moved vertexes according to an original connection relationship to acquire an updated layout; verifying electrical characteristics of the updated layout, determining whether a deviation of the electrical characteristics of the updated layout is within a tolerable range, and performing geometric correction to compensate for a deviation of electrical parameters if no is determined and then ending correction, or ending the correction if yes is determined.

An improved methods and systems for detecting defect(s) on a mask are disclosed. An improved method comprises inspecting an exposed wafer after the wafer was exposed, by a lithography system using a mask, with a selected process condition that is determined based on a mask defect printability under the selected process condition; and identifying, based on the inspection, a wafer defect that is caused by a defect on the mask to enable identification of the defect on the mask.

According to one embodiment, a pattern inspection method includes detecting a region of a photomask having a pattern that differs from a corresponding design, acquiring an exposure focus shift information including an exposure focus shift amount of a portion of a substrate corresponding to the detected region of the photomask. The exposure focus shift amount for the detected region is acquired from the exposure focus shift information, and then a pass/fail determination for the detected region is performed based on an estimated pattern to be formed on the substrate.

A method for detecting deposited particles (P) on a surface (11) of an object (3, 14) includes: irradiating a partial region of the surface (11) of the object (3, 14) with measurement radiation; detecting measurement radiation scattered on the irradiated partial region, and detecting particles in the partial region of the surface of the object (3, 14) based on the detected measurement radiation. In the steps of irradiating and detecting, the surface (11) of the object (3, 14) has an anti-reflective coating (13) and/or a surface structure (15) for reducing the reflectivity of the surface (11) for the measurement radiation (9), wherein the particle detection limit is lowered due to the anti-reflective coating (13) and/or the surface structure (15). Also disclosed are a wafer (3) and a mask blank for carrying out the method.

An inspection apparatus for inspecting an object such as a pellicle for use in an EUV lithographic apparatus, the inspection apparatus including: a vacuum chamber; a load lock forming an interface between the vacuum chamber and an ambient environment; and a stage apparatus configured to receive the object from the load lock and displace the object inside the vacuum chamber, wherein the vacuum chamber comprises a first parking position and a second parking position for temporarily storing the object.

Systems and methods for determining a diagnosis of a screening system are disclosed. Such systems and methods include identifying defect results based on inline characterization tool data, identifying electrical test results based on electrical test data, generating one or more correlation metrics based on the defect results and the electrical test results, and determining at least one diagnosis of the screening system based on the one or more correlation metrics, the diagnosis corresponding to a performance of the screening system.

An inspection tool comprises an imaging system configured to image a portion of a semiconductor substrate. The inspection tool may further comprise an image analysis system configured to obtain an image of a structure on the semiconductor substrate from the imaging system, encode the image of the structure into a latent space thereby forming a first encoding. the image analysis system may subtract an artifact vector, representative of an artifact in the image, from the encoding thereby forming a second encoding; and decode the second encoding to obtain a decoded image.

A data processing device for detecting defects in sample image data generated by a charged particle assessment system, the device comprising: a first processing module configured to receive a sample image datastream from the charged particle assessment system, the sample image datastream comprising an ordered series of data points representing an image of the sample, and to apply a first defect detection test to select a subset of the sample image datastream as first selected data, wherein the first defect detection test is a localised test which is performed in parallel with receipt of the sample image datastream; and a second processing module configured to receive the first selected data and to apply a second defect detection test to select a subset of the first selected data as second selected data.

A computer-implemented defect prediction method for a device manufacturing process involving processing a pattern onto a substrate. Non-correctable error is used to help predict locations where defects are likely to be present, allowing improvements in metrology throughput. In an embodiment, non-correctable error information relates to imaging error due to limitations on, for example, the lens hardware, imaging slit size, and/or other physical characteristics of the lithography system. In an embodiment, non-correctable error information relates to imaging error induced by lens heating effects.

A method for determining a process window of a patterning process based on a failure rate. The method includes obtaining a plurality of features printed on a substrate, grouping, based on a metric, the features into a plurality of groups, and generating, based on measurement data associated with a group of features, a base failure rate model for the group of features, wherein the base failure rate model identifies the process window related to the failure rate of the group of features. The method can further include generating, using the base failure rate model, a feature-specific failure rate model for a specific feature, wherein the feature-specific failure rate model identifies a feature-specific process window such that an estimated failure rate of the specific feature is below a specified threshold.