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: A system for characterizing a specimen is disclosed. In one embodiment, the system includes a controller configured to: receive training images of one or more defects of the specimen; generate a machine learning classifier based on the training images; receive product images of one or more defects of a specimen; determine one or more defect type classifications of one or more defects with the machine learning classifier; filter the product images with one or more smoothing filters; perform binarization processes to generate binarized product images; perform morphological image processing operations on the binarized product images; determine one or more algorithm-estimated defect sizes of the one or more defects based on the binarized product images; and determine one or more refined estimates of one or more defect sizes of the one or more defects based on the one or more algorithm-estimated defect sizes and the one or more defect type classifications.

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: 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: A system for characterizing a specimen is disclosed. In one embodiment, the system includes a controller configured to: receive training images of one or more defects of the specimen; generate a machine learning classifier based on the training images; receive product images of one or more defects of a specimen; determine one or more defect type classifications of one or more defects with the machine learning classifier; filter the product images with one or more smoothing filters; perform binarization processes to generate binarized product images; perform morphological image processing operations on the binarized product images; determine one or more algorithm-estimated defect sizes of the one or more defects based on the binarized product images; and determine one or more refined estimates of one or more defect sizes of the one or more defects based on the one or more algorithm-estimated defect sizes and the one or more defect type classifications.

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: 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: Disclosed are methods and apparatus for inspecting a photolithographic reticle. Modeled images of a plurality of target features of the reticle are obtained based on a design database for fabricating the reticle. An inspection tool is used to obtain a plurality of actual images of the target features of the reticle. The modelled and actual images are binned into a plurality of bins based on image properties of the modelled and actual images, and at least some of the image properties are affected by one or more neighbor features of the target features on the reticle in a same manner. The modelled and actual images from at least one of the bins are analyzed to generate a feature characteristic uniformity map for the reticle.

Abstract: Disclosed are methods and apparatus for inspecting a photolithographic reticle. Modeled images of a plurality of target features of the reticle are obtained based on a design database for fabricating the reticle. An inspection tool is used to obtain a plurality of actual images of the target features of the reticle. The modelled and actual images are binned into a plurality of bins based on image properties of the modelled and actual images, and at least some of the image properties are affected by one or more neighbor features of the target features on the reticle in a same manner. The modelled and actual images from at least one of the bins are analyzed to generate a feature characteristic uniformity map for the reticle.