Abstract: A repair system for flat panel display (FPD) substrates performs a number of operations, such as automatic image capture and processing, automatic defect classification, automatic repair classification, and repair macro (instruction) generation software. Defect classification, repair classification, and repair macro generation are based on an open architecture and can address any number of use-cases through the use of multi-tiered classifiers, and thus a wide variety of panel designs may be repaired within a single repair tool. The multi-tiered set of classifiers, e.g., defect classifier, repair classifier, enables an efficient decision-making repair process with capability for customization. The multi-tiered classifiers are optionally extended to support statistical learning (both online & batch) and active learning, in the context of a supporting database of defects and associated tools.

Abstract: A repair system for flat panel display (FPD) substrates performs a number of operations, such as automatic image capture and processing, automatic defect classification, automatic repair classification, and repair macro (instruction) generation software. Defect classification, repair classification, and repair macro generation are based on an open architecture and can address any number of use-cases through the use of multi-tiered classifiers, and thus a wide variety of panel designs may be repaired within a single repair tool. The multi-tiered set of classifiers, e.g., defect classifier, repair classifier, enables an efficient decision-making repair process with capability for customization. The multi-tiered classifiers are optionally extended to support statistical learning (both online & batch) and active learning, in the context of a supporting database of defects and associated tools.

Abstract: A method and system for defect inspection of microfabricated structures such as semiconductor wafers, masks or reticles for micro-fabrication, flat panel displays, micro-electro-mechanical (MEMs) having repetitive array regions such as memories or pixels. In one embodiment a method of inspection of microfabricated structures includes the steps of acquiring contrast data or images from the microfabricated structures, analyzing automatically the contrast data or images to find repetitive regions of the contrast data and comparing the repetitive regions of the contrast data with reference data to detect defects in the microfabricated structures. In the analyzing step, a cell-metric such as the range, or mean or other statistical or mathematical measure of the contrast data is used to find the repetitive regions. Image or contrast data acquisition can be performed with an optical, e-beam or other microscope suited for microfabricated structures.

Abstract: A method and system for defect inspection of microfabricated structures such as semiconductor wafers, masks or reticles for micro-fabrication, flat panel displays, micro-electro-mechanical (MEMs) having repetitive array regions such as memories or pixels. In one embodiment a method of inspection of microfabricated structures includes the steps of acquiring contrast data or images from the microfabricated structures, analyzing automatically the contrast data or images to find repetitive regions of the contrast data and comparing the repetitive regions of the contrast data with reference data to detect defects in the microfabricated structures. In the analyzing step, a cell-metric such as the range, or mean or other statistical or mathematical measure of the contrast data is used to find the repetitive regions. Image or contrast data acquisition can be performed with an optical, e-beam or other microscope suited for microfabricated structures.

Abstract: 10A method is described for detecting and characterizing defects on a test surface of a semiconductor wafer. A three-dimensional image of the test surface is aligned and compared with a three-dimensional image of a defect-free reference surface. Intensity differences between corresponding pixels in the two images that exceed a predefined threshold value are deemed defect pixels. According to the method, the pixels of the reference image are grouped according to their respective z values (elevation) to identify different physical layers of the reference surface. Because different surface layers can have different image properties, such as reflectance and image texture, the groups of pixels are analyzed separately to determine an optimal threshold value for each of the groups, and therefore for each layer of the reference surface.

Abstract: A method is described for optimizing intensity-comparison thresholds used to compare test and reference images for defect detection. Intensity differences between corresponding pixels in the two images that exceed a predefined threshold value are deemed defect pixels. According to the method, the pixels of the reference image are grouped according to their respective z values (elevation) to identify different physical layers of the reference surface. Because different surface layers can have different image properties, such as reflectance and image texture, the groups of pixels are analyzed separately to determine an optimal threshold value for each of the groups, and therefore for each layer of the reference surface.