## 1 Introduction  
For most manual alignment systems, operators easily become fatigued after a long working period using the optical inspection system. Human errors are the source of lack of control and instability in manual assembling. Therefore, manual alignment accuracy cannot be guaranteed due to the operation variations of different workers at different times. To increase assembling accuracy and alignment stability, automatic image-servo systems have been proposed [1; 2; 3; 4; 5]. [PERSON] and [PERSON] proposed an image-based visual servo control to study the automatic assembling task using the relational graph error signals [1]. [PERSON] _et al._ used two-stage alignment techniques to find the fiducial mark and used two CCDs to check the wafer bonding alignment [2]. Later, [PERSON] _et al._ studied a neural network method for quick wafer alignment to reduce complexity of motion analysis [3]. To achieve mask alignment tasks rapidly and accurately using automatic optical inspection (AOI) technologies, there are three key points which affect the cycle time and positioning accuracy. The three issues are (1) the fiducial mark design; (2) image processing and pattern recognition;and (3) image-servo motion control. For the design of fiducial mark, cross and circular marks are usually used in alignment applications, such as high-density laser-fiber module packaging tasks, semiconductor wafer alignment systems, and automatic alignment systems for I-line stepper. [PERSON] _et al._ explored a novel method for packaging a laser-fiber module with a passive method based on registration principles of photolithography; they proposed the alignment method using the fiducial marks of crosses [6]. [PERSON] and [PERSON] studied a sub-micro registration between circularly symmetric fiducial marks to find their centroids by the second-derivative zero-crossing method [7]. [PERSON] and [PERSON] were devoted to obtaining a proper fiducial mark design that optimizes the reliability of robotic manipulation; they studied several fiducial mark shapes via ophthalmic lenses in alignment processes [8]. The general alignment system contains one, two or more charge coupled device (CCD) cameras to guide a high-precision positioning stage. [PERSON] _et al._ presented a precision alignment system integrated with machine vision, consisting of a stacked XY positioning stage driven by piezoelectric ceramic motors [9]. The above research used a circular fiducial mark as the alignment marks captured by a CCD camera and the positional error was below 60 nm. [PERSON] _et al._ presented a micro-assembly system based on vision-servo guidance system, which incorporates two vision sensors to guide the \(XY\theta z\) stage to perform the coarse positioning task and the fine positioning task. The circular fiducial mark is used as the coarse positioning alignment and the cross fiducial mark is used for the fine alignment task. The designed image recognition system is integrated into the micro-assembling system with image-servo to achieve the precise assembling task automatically, and the repeatable precision was better than 10 \(\upmu\)m [10]. [PERSON] _et al._ presented a real time critical dimension measurement of pattern on TFT-LCD and the searching time for \(XY\theta z\) was about 12 ms with repeatability of less than 30 nm [11]. [PERSON] and [PERSON] used a cross symbol as the fiducial mark of their proposed vision-servo alignment system and the alignment error was less than 1 \(\upmu\)m [12]. However, their angular alignment accuracy was worse than two CCD cameras' alignment due to low angular resolution using one CCD camera. The alignment using the stacked \(XY\theta z\) stage usually causes cumulative errors, such as parallelism, orthogonal errors between axes and flatness error. To address the above errors, the coplanar \(XY\theta z\) stages have been proposed and they show good positioning accuracy in many studies [9; 10].