Abstract: An inspection system may generate first-step images of multiple sample regions after a first process step and generate second-step images of the sample regions after a second process step, where the second process step modifies the sample in at least one of the sample regions. The system may further identify one of the sample regions as a test region and at least some of the remaining sample regions as comparison regions, where the second-step image of the test region is a test image and the second-step images of the comparison regions are comparison images. The system may further generate a multi-step difference image by subtracting a combination of at least one of the second-step comparison images and at least two of the first-step images from the test image. The system may further identify defects in the test region associated with the second process step based on the multi-step difference image.

Abstract: An inspection system includes a controller including one or more processors configured to execute program instructions. The program instructions cause the one or more processors to receive at least a first portion of a first set of repeat swaths of a first scan path of a sample. The program instructions cause the one or more processors to generate an image by averaging the first portion of the first set of repeat swaths. Averaging the first portion of the first set of repeat swaths reduces a noise in the image. The program instructions cause the one or more processors to detect one or more defects in an inspection region of the sample using the image.

Abstract: An inspection system may develop an inspection recipe by generating N inspection images of a preliminary sample with one or more optical inspection sub-systems associated with N different optical inspection modes, generating probabilities that each of the locations of the preliminary sample are in background or defect classes using a classifier with the inspection images from at least some combinations of a number M of the optical inspection modes, where M is greater than one and less than N and corresponds to a number of the optical inspection modes to include in the inspection recipe, and selecting one of the combinations of M of the optical inspection modes based on a metric describing a distinction between the background and defect classes. The inspection system may further identify defects on a test sample using M inspection images generated with the selected combination of M of the optical inspection modes.

Abstract: An inspection system may generate first-step images of multiple sample regions after a first process step and generate second-step images of the sample regions after a second process step, where the second process step modifies the sample in at least one of the sample regions. The system may further identify one of the sample regions as a test region and at least some of the remaining sample regions as comparison regions, where the second-step image of the test region is a test image and the second-step images of the comparison regions are comparison images. The system may further generate a multi-step difference image by subtracting a combination of at least one of the second-step comparison images and at least two of the first-step images from the test image. The system may further identify defects in the test region associated with the second process step based on the multi-step difference image.

Abstract: Methods and systems for setting up inspection of a specimen are provided. One system includes one or more computer subsystems configured for acquiring a reference image for a specimen and modifying the reference image to fit the reference image to a design grid thereby generating a golden grid image. The one or more computer subsystems are also configured for storing the golden grid image for use in inspection of the specimen. The inspection includes aligning a test image of the specimen generated from output of an inspection subsystem to the golden grid image.

Abstract: A system includes a processing unit communicatively coupled to a detector array of an optical wafer characterization system. The processing unit is configured to perform one or more steps of a method or process including the steps of acquiring one or more target images of a target location on a wafer from the detector array, applying a de-noising filter to at least the one or more target images, determining one or more difference images from one or more reference images and the one or more target images, and up-sampling the one or more difference images to generate one or more up-sampled images. One or more wafer defects are detectable in the one or more difference images or the up-sampled images.

Abstract: A system is disclosed. In one embodiment, the system includes a scanning electron microscopy sub-system including an electron source configured to generate an electron beam and an electron-optical assembly including one or more electron-optical elements configured to direct the electron beam to the specimen. In another embodiment, the system includes one or more grounding paths coupled to the specimen, the one or more grounding paths configured to generate one or more transmission signals based on one or more received electron beam-induced transmission currents. In another embodiment, the system includes a controller configured to: generate control signals configured to cause the scanning electron microscopy sub-system to scan the portion of the electron beam across a portion of the specimen; receive the transmission signals via the one or more grounding paths; and generate transmission current images based on the transmission signals.

Abstract: Disclosed are methods and apparatus for inspecting a photolithographic reticle. A plurality of reference far field images are simulated by inputting a plurality of reference near field images into a physics-based model, and the plurality of reference near field images are generated by a trained deep learning model from a test portion of the design database that was used to fabricate a test area of a test reticle. The test area of a test reticle, which was fabricated from the design database, is inspected for defects via a die-to-database process that includes comparing the plurality of reference far field reticle images simulated by the physic-based model to a plurality of test images acquired by the inspection system from the test area of the test reticle.

Abstract: Systems and methods increase the signal to noise ratio of optical inspection of wafers to obtain higher inspection sensitivity. The computed reference image can minimize a norm of the difference of the test image and the computed reference image. A difference image between the test image and a computed reference image is determined. The computed reference image includes a linear combination of a second set of images.

Abstract: Methods and systems for setting up inspection of a specimen are provided. One system includes one or more computer subsystems configured for acquiring a reference image for a specimen and modifying the reference image to fit the reference image to a design grid thereby generating a golden grid image. The one or more computer subsystems are also configured for storing the golden grid image for use in inspection of the specimen. The inspection includes aligning a test image of the specimen generated from output of an inspection subsystem to the golden grid image.

Abstract: Disclosed are methods and apparatus for inspecting a photolithographic reticle. A near field reticle image is generated via a deep learning process based on a reticle database image produced from a design database, and a far field reticle image is simulated at an image plane of an inspection system via a physics-based process based on the near field reticle image. The deep learning process includes training a deep learning model based on minimizing differences between the far field reticle images and a plurality of corresponding training reticle images acquired by imaging a training reticle fabricated from the design database, and such training reticle images are selected for pattern variety and are defect-free.

Abstract: In one embodiment, disclosed are apparatus, methods, and targets for determining a phase shift of a photomask having a phase-shift target. An inspection or metrology system is used to direct an incident beam towards the target and then detect a plurality of intensity measurements that are transmitted through the target in response to the incident beam. A phase shift value for the target may then be determined based on the intensity measurements.

Abstract: Context attributes for optical imaging of a patterned layer of a semiconductor die are calculated. Calculating the context attributes includes calculating convolutions of a pattern of the patterned layer with respective kernels of a plurality of kernels, wherein the plurality of kernels is orthogonal. Defects on the semiconductor die are found in accordance with the context attributes.

Abstract: Disclosed is a system that includes a light source for generating an illumination beam and an illumination lens system for directing the illumination beam towards a sample. The system further includes a collection lens system for directing towards a detector output light from the sample in response to the illumination beam and a detector for receiving the output light from the sample. The collection lens system includes a fixed-design compensator plate having individually selectable filters with different configurations for correcting system aberration of the system under different operating conditions. The system also includes a controller operable for: (i) generating and directing the illumination beam towards the sample, (ii) selecting operating conditions and a filter for correcting the system aberration under such selected operating conditions, (iii) generating an image based on the output light, and (iv) determining whether the sample passes inspection or characterizing such sample based on the image.

Abstract: Block-to-block reticle inspection includes acquiring a swath image of a portion of a reticle with a reticle inspection sub-system, identifying a first occurrence of a block in the swatch image and at least a second occurrence of the block in the swath image substantially similar to the first occurrence of the block and determining at least one of a location, one or more geometrical characteristics of the block and a spatial offset between the first occurrence of the block and the at least a second occurrence of the block.

Abstract: Disclosed are methods and apparatus for qualifying a photolithographic reticle. A reticle inspection tool is used to acquire a plurality of images at different imaging configurations from each of a plurality of pattern areas of a test reticle. A reticle near field is recovered for each of the pattern areas of the test reticle based on the acquired images from each pattern area of the test reticle. The recovered reticle near field is then used to determine whether the test reticle or another reticle will likely result in unstable wafer pattern or a defective wafer.

Abstract: Disclosed are methods and apparatus for inspecting a photolithographic reticle. A near field reticle image is generated via a deep learning process based on a reticle database image produced from a design database, and a far field reticle image is simulated at an image plane of an inspection system via a physics-based process based on the near field reticle image. The deep learning process includes training a deep learning model based on minimizing differences between the far field reticle images and a plurality of corresponding training reticle images acquired by imaging a training reticle fabricated from the design database, and such training reticle images are selected for pattern variety and are defect-free.

Abstract: Disclosed are methods and apparatus for qualifying a photolithographic reticle. A reticle inspection tool is used to acquire images at different imaging configurations from each of a plurality of pattern areas of a test reticle. A reticle near field for each of the pattern areas of the test reticle is recovered based on the acquired images from each pattern area of the test reticle. A lithography model is applied to the reticle near field for the test reticle to simulate a plurality of test wafer images, and the simulated test wafer images are analyzed to determine whether the test reticle will likely result in an unstable or defective wafer.

Abstract: Disclosed are methods and apparatus for facilitating an inspection of a sample using an inspection tool. An inspection tool is used to obtain an image or signal from an EUV reticle that specifies an intensity variation across the EUV reticle, and this intensity variation is converted to a CD variation that removes a flare correction CD variation so as to generate a critical dimension uniformity (CDU) map without the flare correction CD variation. This removed flare correction CD variation originates from design data for fabricating the EUV reticle, and such flare correction CD variation is generally designed to compensate for flare differences that are present across a field of view (FOV) of a photolithography tool during a photolithography process. The CDU map is stored in one or more memory devices and/or displayed on a display device, for example, of the inspection tool or a photolithography system.

Abstract: In one embodiment, disclosed are apparatus, methods, and targets for determining a phase shift of a photomask having a phase-shift target. An inspection or metrology system is used to direct an incident beam towards the target and then detect a plurality of intensity measurements that are transmitted through the target in response to the incident beam. A phase shift value for the target may then be determined based on the intensity measurements.