This study proposes an auto focusing method for multi-focus images in assembling lens modules in digital camera phones. A camera module in a camera phone is composed of a CCD sensor, an IR glass, a lens and a PCB board. The alignment of the components is one of the most important factors in determining the product quality. Auto-focus is essential to adjust the image quality of the IR glass, but there are two focal points in the captured image, due to the thickness of the IR glass. Therefore, the sharpness, probability and scale factor are used to find the desired focus from a multi-focus image. The sharpness is defined as the clarity of the image. The variation of the probability and scale factors are analyzed to find the focus after pattern recognition with a registered image. The presented algorithm was applied to a lens assembly machine which has 5 axes, two vacuum chucks and an inspection system. The desired focus can be determined on the local maximum of the sharpness, the probability and the scale factor in the experiment.

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Camera phones are very popular these days and mass production is essential for manufacturing the camera modules used in the phones. This study aims at developing a focusing method for use in the camera module assembly process. The camera module is composed of small components and machine vision is necessary to align them before their assembly. The inspected images usually have multiple areas with different focal distances, due to the different thicknesses of the components. Sharpness is the evaluation of focus, calculated by determining its absolute derivative and used for determining the focal point. A dynamic focus region(DFR) is defined to find the object of interest to be focused on. The DFR is built from the edge fragments detected using several narrow ROIs across the vision screen. An assembly machine was constructed to attach lens mounts to a PCB for the experiment. The distance between the lens mount and vision camera was adjusted to find the focus position by means of the sharpness and DFR. The result shows that the desired object is brought into focus and the proposed method can be applied to the camera module assembly process.

This study proposes a simple and fast technique wafer can be employed as alignment marks. However, they for detecting wafer cutting lines using the Affine transformation are not available sometimes because the size of the marks before sawing. This technique was developed for the purpose is insufficient or their quality is inadequate. In some cases of wafer alignment when alignment marks are unavailable. i their qusablity sinadequate. Is en case The edges of the pre-sawing lines are complex and there are there are no patterns usable patterns on the wafer. When this bright shapes in the sawing lines, so it is difficult to apply happens, we can consider using the intersection of the cutting the conventional edge finding method. The sawing lines are lines. The cutting lines are usually called pre-sawing lines. represented by rectangular windows which are deformed by an The information that should be detected is the misalignment Affine transformation. The amount of deformation is determined in the xyO directions. The pre-sawing lines may be detected when the grey level in the window is a global minimum in the image. The global minimum is found by the golden section search. byledged or blo searchng However, these methods are The algorithm was tested using 13 sample images and repeated influenced by the patterns around the lines and a substantial 10 times for each image. The results showed that the processing processing time is needed to determine the rotational angle, time was below 1200msec and the deviation was in the sub-pixel 0. The result also needs to be highly precise and reliable, level. We found that the proposed method can be applied to align but these methods are not always satisfactory in this respect. wafers in the dicing process. Therefore, this study proposes a method of detecting the pre-