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Mask alignment of photolithography technology is used in many applications, such as micro electro mechanical systems’ semiconductor process, printed circuits board, and flat panel display. As the dimensions of the product are getting smaller and smaller, the automatic mask alignment of photolithography is becoming more and more important. The traditional stacked XY-Θz stage is heavy and it has cumulative flatness errors due to its stacked assembly mechanism. The XXY stage has smaller cumulative error due to its coplanar design and it can move faster than the traditional XY-Θz stage. However, the relationship between the XXY stage’s movement and the commands of the three motors is difficult to compute, because the movements of the three motors on the same plane are coupling. Therefore, an artificial neural network is studied to establish a nonlinear mapping from the desired position and orientation of the stage to three motors’ commands. Further, this paper proposes an image-servo automatic mask alignment system, which consists of a coplanar XXY stage, dual GIGA-E CCDs with lens and a programmable automatic controller (PAC). Before preforming the compensation, a self-developed visual-servo provides the positioning information which is obtained from the image processing and pattern recognition according to the specified fiducial marks. To obtain better precision, two methods including the center of gravity method and the generalize Hough Transformation are studied to correct the shift positioning error

Design of an Image-Servo Mask Alignment System Using Dual CCDs with an XXY Stage