Abstract: A technique for determining photo-mask defect disposition is described. In this technique, a target mask pattern is used to expand an initial region in a photo-mask that is included in an initial mask-inspection image. In particular, a revised mask-inspection image that includes the initial region and a region surrounding the initial region is generated based on the initial mask-inspection image and the target mask pattern. Then a corresponding simulated mask pattern is calculated in an inverse optical calculation using the revised mask-inspection image and an optical model of the mask-inspection system. This simulated mask pattern is used to simulate a wafer pattern in a photo-lithographic process, and disposition of a possible defect in the initial region is subsequently determined based on the simulated wafer pattern and a target wafer pattern.

Abstract: Embodiments of a computer system, a process, a computer-program product (i.e., software), and a data structure or a file for use with the computer system are described. These embodiments may be used to determine or generate source patterns that define illumination patterns on photo-masks during a photolithographic process. Moreover, a given source pattern may be determined concurrently with an associated mask pattern (to which a given photo-mask corresponds) or sequentially (i.e., either the given source pattern may be determined before the associated mask pattern or vice versa.). During the determining, the given source pattern may be represented using one or more level-set functions. Additionally, the source pattern may be determined using an Inverse Lithography (ILT) calculation.

Abstract: A medical diagnostic ultrasound imaging system aligns substantially co-planar two-dimensional images to form an extended field of view using improved methods that speed the alignment, use Doppler processors to alert the user when transducer motion relative to the imaged region is excessive, and provide a longitudinal sectional view of a blood vessel and surrounding tissue.

Abstract: A simulated wafer image of a physical mask and a defect-free reference image are used to generate a severity score for each defect, thereby giving a customer meaningful information to accurately assess the consequences of using a mask or repairing that mask. The defect severity score is calculated based on a number of factors relating to the changes in critical dimensions of the neighbor features to the defect. A common process window can also be used to provide objective information regarding defect printability. Certain other aspects of the mask relating to mask quality, such as line edge roughness and contact corner rounding, can also be quantified by using the simulated wafer image of the physical mask.

Abstract: A medical diagnostic ultrasonic imaging system acquires image data for at least two frames at each of multiple positions, each frame identified with a respective phase of a physiological cycle. A multiphase 3-D or extended field of view data set is constructed from the image data. Then a plurality of images are generated from the multiphase data set. Each image is associated with a respective phase of the physiological cycle, and these images are displayed in sequence to a user.

Abstract: A reference image is generated from a subject image of at least a portion of a photolithography mask to enable a photolithography mask inspection and analysis system that otherwise cannot generate a reference image from a reference die or digitized design data, for example, to perform a mask analysis using the reference image. A mask inspection and analysis system may then be enhanced to perform one or more additional mask analyses to analyze the mask. The reference image is generated by identifying a defect or contaminant of the mask in the subject image and modifying the subject image to remove the defect or contaminant from the mask to generate the reference image. For one embodiment, a system using a STARlight inspection tool that captures transmitted and reflected images of a portion of a mask may then be enhanced to perform one or more mask analyses that use a reference image.

Abstract: Embodiments of a method for determining a mask pattern to be used on a photo-mask in a lithography process are described. This method may be performed by a computer system. During operation, this computer system receives at least a portion of a first mask pattern including first regions that violate pre-determined rules associated with the photo-mask. Next, the computer system determines a second mask pattern based on at least the portion of the first mask pattern, where the second mask pattern includes second regions that are estimated to comply with the pre-determined rules. Note that the second regions correspond to the first regions, and the second mask pattern is determined using a different technique than that used to determine the first mask pattern.

Abstract: Serious defects on a mask can compromise the functionality of the integrated circuits formed on the wafer. Nuisance defects, which do not affect the functionality, waste expensive resources. A defect analysis tool with job-based automation can accurately and efficiently determine defect printability. This tool can run a job, using a mask file, to simulate the wafer exposure that the mask would provide under a given set of parameters. These parameters can relate to the mask itself, the inspection system used to create the mask file, and the stepper that can be used to expose the mask. The processes performed during the job can be done uniformly for defects on the mask. This uniformity allows the tool to efficiently run multiple jobs. The results of the job can be presented using different levels of detail to facilitate user review.

Abstract: A simulated wafer image of a physical mask and a defect-free reference image are used to generate a severity score for each defect, thereby giving a customer meaningful information to accurately assess the consequences of using a mask or repairing that mask. The defect severity score is calculated based on a number of factors relating to the changes in critical dimensions of the neighbor features to the defect. A common process window can also be used to provide objective information regarding defect printability. Certain other aspects of the mask relating to mask quality, such as line edge roughness and contact corner rounding, can also be quantified by using the simulated wafer image of the physical mask.

Abstract: A simulated wafer image of a physical mask and a defect-free reference image are used to generate a severity score for each defect, thereby giving a customer meaningful information to accurately assess the consequences of using a mask or repairing that mask. The defect severity score is calculated based on a number of factors relating to the changes in critical dimensions of the neighbor features to the defect. A common process window can also be used to provide objective information regarding defect printability. Certain other aspects of the mask relating to mask quality, such as line edge roughness and contact corner rounding, can also be quantified by using the simulated wafer image of the physical mask.

Abstract: A simulated wafer image of a physical mask and a defect-free reference image are used to generate a severity score for each defect, thereby giving a customer meaningful information to accurately assess the consequences of using a mask or repairing that mask. The defect severity score is calculated based on a number of factors relating to the changes in critical dimensions of the neighbor features to the defect. A common process window can also be used to provide objective information regarding defect printability. Certain other aspects of the mask relating to mask quality, such as line edge roughness and contact corner rounding, can also be quantified by using the simulated wafer image of the physical mask.

Abstract: A simulated wafer image of a physical mask and a defect-free reference image are used to generate a severity score for each defect, thereby giving a customer meaningful information to accurately assess the consequences of using a mask or repairing that mask. The defect severity score is calculated based on a number of factors relating to the changes in critical dimensions of the neighbor features to the defect. A common process window can also be used to provide objective information regarding defect printability. Certain other aspects of the mask relating to mask quality, such as line edge roughness and contact corner rounding, can also be quantified by using the simulated wafer image of the physical mask.

Abstract: A simulated wafer image of a physical mask and a defect-free reference image are used to generate a severity score for each defect, thereby giving a customer meaningful information to accurately assess the consequences of using a mask or repairing that mask. The defect severity score is calculated based on a number of factors relating to the changes in critical dimensions of the neighbor features to the defect. A common process window can also be used to provide objective information regarding defect printability. Certain other aspects of the mask relating to mask quality, such as line edge roughness and contact corner rounding, can also be quantified by using the simulated wafer image of the physical mask.

Abstract: A simulated wafer image of a physical mask and a defect-free reference image are used to generate a severity score for each defect, thereby giving a customer meaningful information to accurately assess the consequences of using a mask or repairing that mask. The defect severity score is calculated based on a number of factors relating to the changes in critical dimensions of the neighbor features to the defect. A common process window can also be used to provide objective information regarding defect printability. Certain other aspects of the mask relating to mask quality, such as line edge roughness and contact corner rounding, can also be quantified by using the simulated wafer image of the physical mask.

Abstract: A reference image is generated from a subject image of at least a portion of a photolithography mask to enable a photolithography mask inspection and analysis system that otherwise cannot generate a reference image from a reference die or digitized design data, for example, to perform a mask analysis using the reference image. A mask inspection and analysis system may then be enhanced to perform one or more additional mask analyses to analyze the mask. The reference image is generated by identifying a defect or contaminant of the mask in the subject image and modifying the subject image to remove the defect or contaminant from the mask to generate the reference image. For one embodiment, a system using a STARlight inspection tool that captures transmitted and reflected images of a portion of a mask may then be enhanced to perform one or more mask analyses that use a reference image.

Abstract: A reference image is generated from a subject image of at least a portion of a photolithography mask to enable a photolithography mask inspection and analysis system that otherwise cannot generate a reference image from a reference die or digitized design data, for example, to perform a mask analysis using the reference image. A mask inspection and analysis system may then be enhanced to perform one or more additional mask analyses to analyze the mask. The reference image is generated by identifying a defect or contaminant of the mask in the subject image and modifying the subject image to remove the defect or contaminant from the mask to generate the reference image. For one embodiment, a system using a STARlight inspection tool that captures transmitted and reflected images of a portion of a mask may then be enhanced to perform one or more mask analyses that use a reference image.

Abstract: Serious defects on a mask can compromise the functionality of the integrated circuits formed on the wafer. Nuisance defects, which do not affect the functionality, waste expensive resources. A defect analysis tool with job-based automation can accurately and efficiently determine defect printability. This tool can run a job, using a mask file, to simulate the wafer exposure that the mask would provide under a given set of parameters. These parameters can relate to the mask itself, the inspection system used to create the mask file, and the stepper that can be used to expose the mask. The processes performed during the job can be done uniformly for defects on the mask. This uniformity allows the tool to efficiently run multiple jobs. The results of the job can be presented using different levels of detail to facilitate user review.

Abstract: Serious defects on a mask can compromise the functionality of the integrated circuits formed on the wafer. Nuisance defects, which do not affect the functionality, waste expensive resources. A defect analysis tool with job-based automation can accurately and efficiently determine defect printability. This tool can run a job, using a mask file, to simulate the wafer exposure that the mask would provide under a given set of parameters. These parameters can relate to the mask itself, the inspection system used to create the mask file, and the stepper that can be used to expose the mask. The processes performed during the job can be done uniformly for defects on the mask. This uniformity allows the tool to efficiently run multiple jobs. The results of the job can be presented using different levels of detail to facilitate user review.

Abstract: To exam mask defect impact during the transfer of a mask pattern to a wafer layer, tools can use mask images obtained during mask inspection. Specifically, these tools can also use optical models of such mask images to simulate wafer images. However, when feature sizes become very small, optical models may not provide sufficiently accurate simulation results. Using a photoresist model would yield significantly more accurate simulation results than using an optical model. Unfortunately, resist modeling is very slow, thereby making it commercially impractical. A simulation tool can generate a simulated wafer image having the accuracy of a photoresist model with the speed of an optical model by using a threshold look-up table. In one embodiment, the threshold look-up table could include variables such as feature size, pitch size, feature type, and defect type.

Abstract: Masks that include optical proximity correction or phase shifting regions are increasingly being used in the manufacturing process. These masks, either initially or after repair, can have “soft” defects, e.g. phase and/or transmission defects. In accordance with one feature of the invention, soft defect information can be computed from standard test images of a mask. This soft defect information can be used to generate an accurate simulated wafer image, thereby providing valuable defect impact information to a user. Knowing the impact of the soft defect can enable a user to make better decisions regarding the mask. Specifically, a user can now with confidence accept the mask for the desired lithographic process, repair the mask at certain critical locations, or reject the mask, all without exposing a wafer.