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. A test area of a test reticle, which is fabricated from the design database, is inspected for defects via a die-to-database process that includes comparing a plurality of references images from a reference far field reticle image to a plurality of test images acquired by the inspection system from the test reticle. The reference far field reticle image is simulated based on a reference near field reticle image that is generated by the trained deep learning model.
Abstract:
An inspection system includes an illumination source configured to generate extreme ultraviolet (EUV) light, illumination optics to direct the EUV light to a sample within a range of off-axis incidence angles corresponding to an illumination pupil distribution, collection optics to collect light from the sample in response to the incident EUV light within a range of collection angles corresponding to an imaging pupil distribution, and a detector configured to receive at least a portion of the light collected by the collection optics. Further, a center of the illumination pupil distribution corresponds to an off-axis incidence angle along a first direction on the sample, and at least one of the illumination pupil distribution or the imaging pupil distribution is non-circular with a size along the first direction shorter than along a second direction perpendicular to the first direction.
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 at least two images at different imaging configurations from each pattern area of the reticle. A reticle pattern is reconstructed based on each at least two images from each pattern area of the reticle. For each reconstructed reticle pattern, a lithographic process with two or more different process conditions is modeled on such reconstructed reticle pattern to generate two or more corresponding modeled test wafer patterns. Each two or more modelled test wafer patterns is analyzed to identify hot spot patterns of the reticle patterns that are susceptible to the different process conditions altering wafer patterns formed with such hot spot patterns.
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 the pattern areas of a calibration reticle. A reticle near field is recovered for each of the pattern areas of the calibration reticle based on the acquired images from each pattern area of the calibration reticle. Using the recovered reticle near field for the calibration reticle, a lithography model for simulating wafer images is generated based on the reticle near field. Images are then acquired at different imaging configurations from each of the pattern areas of a test reticle. A reticle near field for the test reticle is then recovered based on the acquired images from the test reticle. The generated 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:
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 at least two images at different imaging configurations from each pattern area of the reticle. A reticle pattern is reconstructed based on each at least two images from each pattern area of the reticle. For each reconstructed reticle pattern, a lithographic process with two or more different process conditions is modeled on such reconstructed reticle pattern to generate two or more corresponding modeled test wafer patterns. Each two or more modelled test wafer patterns is analyzed to identify hot spot patterns of the reticle patterns that are susceptible to the different process conditions altering wafer patterns formed with such hot spot patterns.
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:
An inspection system includes an illumination source configured to generate extreme ultraviolet (EUV) light, illumination optics to direct the EUV light to a sample within a range of off-axis incidence angles corresponding to an illumination pupil distribution, collection optics to collect light from the sample in response to the incident EUV light within a range of collection angles corresponding to an imaging pupil distribution, and a detector configured to receive at least a portion of the light collected by the collection optics. Further, a center of the illumination pupil distribution corresponds to an off-axis incidence angle along a first direction on the sample, and at least one of the illumination pupil distribution or the imaging pupil distribution is non-circular with a size along the first direction shorter than along a second direction perpendicular to the first direction.
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.