A novel pattern error detecting algorithm for SEM images of mask monitoring patterns

In mask fabrication, e-beam exposure equipment malfunctioning could produce erroneous masks, several consecutive mask failures in the worst case. This type of error might unexpectedly increase mask turnaround time. Due to high cost of mask fabrication and its annual growth, it is critical detecting those errors as early as possible. Since mask SEM images at after-development inspection (ADI) phase have more visible noise, edges might be hard to detect clearly using classical edge detection algorithms. In this context, we present a novel pattern error detecting algorithm to capture pattern errors in mask monitoring patterns by inspecting mask SEM images at ADI phase. The originality of this paper lies in its use of simple but powerful techniques in a series used for automated error detection. More specifically, we inspect two specific types of errors in SEM images of monitoring patterns: bridging errors in a chessboard pattern, and CD uniformity errors in a line-and-space pattern. For a chessboard pattern, we utilize both horizontal and vertical projections of image intensity histogram to find areas for inspection automatically. From one dimensional projection of the image, we identify spatial coordinates of our interests, and define a small rectangular region, called D-region. For each D-region, we determine whether a pattern bridge is likely to occur, based on the ratio of brighter pixels in it. For a line-and-space pattern, we compute base lines for CD measurement, and detect CD uniformity errors or line shift errors by applying similar one dimensional histogram analysis and CD-computation algorithm to the image. Our experimental results using real pattern images and programmed defect images support that this technique is effective and robust in detecting errors without layout data or another SEM image for comparison.