Abstract: An improved technique for determining height difference in patterns provided on semiconductor wafers uses real measurements (e.g., measurements from SEM images) and a height difference determination model. In one version of the model, a measurable variable of the model is expressed in terms of a function of a change in depth of shadow (i.e. relative brightness), wherein the depth of shadow depends on the height difference as well as width difference between two features on a semiconductor wafer. In another version of the model, the measurable variable is expressed in terms of a function of a change of a measured distance between two characteristic points on the real image of a periodic structure with respect to a change in a tilt angle of a scanning electron beam.

Abstract: An improved technique for determining height difference in patterns provided on semiconductor wafers uses real measurements (e.g., measurements from SEM images) and a height difference determination model. In one version of the model, a measurable variable of the model is expressed in terms of a function of a change in depth of shadow (i.e. relative brightness), wherein the depth of shadow depends on the height difference as well as width difference between two features on a semiconductor wafer. In another version of the model, the measurable variable is expressed in terms of a function of a change of a measured distance between two characteristic points on the real image of a periodic structure with respect to a change in a tilt angle of a scanning electron beam.

Abstract: An improved technique for determining height difference in patterns provided on semiconductor wafers uses real measurements (e.g., measurements from SEM images) and a height difference determination model. In one version of the model, a measurable variable of the model is expressed in terms of a function of a change in depth of shadow (i.e. relative brightness), wherein the depth of shadow depends on the height difference as well as width difference between two features on a semiconductor wafer. In another version of the model, the measurable variable is expressed in terms of a function of a change of a measured distance between two characteristic points on the real image of a periodic structure with respect to a change in a tilt angle of a scanning electron beam.

Abstract: A height of a pattern on a semiconductor wafer is determined by comparing a measured image of the pattern with a predicted image of the pattern, as produced by a shadow model. An estimated height of the pattern is provided as an input to the shadow model. The shadow model produces occluding contours that are used to generate predicted images. A set of predicted images are generated, each predicted image being associated with an estimated height. The estimated height corresponding to the predicted image most closely matching with the measured image is used as the height calculated by the shadow model.

Abstract: An improved technique for determining height difference in patterns provided on semiconductor wafers uses real measurements (e.g., measurements from SEM images) and a height difference determination model. In one version of the model, a measurable variable of the model is expressed in terms of a function of a change in depth of shadow (i.e. relative brightness), wherein the depth of shadow depends on the height difference as well as width difference between two features on a semiconductor wafer. In another version of the model, the measurable variable is expressed in terms of a function of a change of a measured distance between two characteristic points on the real image of a periodic structure with respect to a change in a tilt angle of a scanning electron beam.

Abstract: A height of a pattern on a semiconductor wafer is determined by comparing a measured image of the pattern with a predicted image of the pattern, as produced by a shadow model. An estimated height of the pattern is provided as an input to the shadow model. The shadow model produces occluding contours that are used to generate predicted images. A set of predicted images are generated, each predicted image being associated with an estimated height. The estimated height corresponding to the predicted image most closely matching with the measured image is used as the height calculated by the shadow model.

Abstract: According to an embodiment of the invention there may be provided a system for assigning lithographic mask inspection process parameters. The system may include a search module, a decision module and a memory module. The memory module may be configured to store an expected image expected to be formed on a photoresist during a lithographic process that involves illuminating the lithographic mask. The search module may be configured to search in the expected image for printable features. The decision module may be configured to assign a first lithographic mask inspection process parameter to lithographic mask areas related to printable features and assign a second lithographic mask inspection process parameter to at least some lithographic mask areas that are not associated with printable features. The second lithographic mask inspection process parameter may differ from the first lithographic mask inspection process parameter.

Abstract: According to an embodiment of the invention there may be provided a system for assigning lithographic mask inspection process parameters. The system may include a search module, a decision module and a memory module. The memory module may be configured to store an expected image expected to be formed on a photoresist during a lithographic process that involves illuminating the lithographic mask. The search module may be configured to search in the expected image for printable features. The decision module may be configured to assign a first lithographic mask inspection process parameter to lithographic mask areas related to printable features and assign a second lithographic mask inspection process parameter to at least some lithographic mask areas that are not associated with printable features. The second lithographic mask inspection process parameter may differ from the first lithographic mask inspection process parameter.

Abstract: Evaluating error sources associated with a mask involves: (i) receiving data representative of multiple images of the mask that were obtained at different exposure conditions; (ii) calculating, for multiple sub-frames of each image of the mask, values of a function of intensities of pixels of each sub-frame to provide multiple calculated values; and (iii) detecting error sources in response to calculated values and in response to sensitivities of the function to each error source.

Abstract: Evaluating error sources associated with a mask involves: (i) receiving data representative of multiple images of the mask that were obtained at different exposure conditions; (ii) calculating, for multiple sub-frames of each image of the mask, values of a function of intensities of pixels of each sub-frame to provide multiple calculated values; and (iii) detecting error sources in response to calculated values and in response to sensitivities of the function to each error source.