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Efficiency determines the profits of the semiconductor producers. So the producers spare no effort to enhance the efficiency of every procedure. The purpose of the paper is to present a method to shorten the time to locate the LED chips on wafer. The method consists of 3 steps. Firstly, image segmentation and blob analyzation are used to predict the positions of potential chips. Then predict the orientations of potential chips based on their dominant orientations. Finally, according to the positions and orientations predicted above, locate the chips precisely based on gradient orientation features. Experiments show that the algorithm is faster than the traditional method we choose to locate the LED chips. Besides, even the orientations of the chips on wafer are of big deviation to the orientation of the template, the efficiency of this method won't be affected.

A high-speed reciprocating operating mechanism with fast running and frequent stopping produces a certain vibration due to inertia. The inherent vibration of this mechanism becomes a key factor affecting positioning accuracy and time. However, the existing vibration identification method is suitable only for static or low-speed structure operations. Therefore, a method of identification for operational modal parameters based on strain response is proposed. This method combines the strain mode with the operational modal analysis method, both of which are verified under the static and operational states, respectively. First, the theoretical background of the identification method is derived. Next, the proposed approach is used to identify the displacement mode shapes and the strain mode shapes of the beam structure with modal parameters that are obtained by experimental modal analysis. These results show that the beam structure presents different displacement mode shapes and strain mode shapes under different constraints. Finally, the displacement mode shapes of the sorting arm are obtained by the method under operation. The recognition results are compared with the computational modal analysis, and the two mode shapes are found to be highly consistent, which verifies that the method is equally reliable during operation. This method expands the application scope of the classic experimental modal analysis and reduces the error because of the additional mass of the actual sensor. [ABSTRACT FROM AUTHOR]

During the testing and sorting of LED chips, traditional methods do not exclude the polycrystalline and fragmentary LED chips from the normal chips well. The purpose of this paper is to propose a new algorithm to solve this problem. The algorithm consists of three steps. Firstly, present a simple but efficient image segmentation method to get blobs. Secondly, analyze the blobs to exclude abnormal blobs and predict the pose (position and orientation) of the potential object based on the pose of the minimum enclosing rectangle (MER) of each remained blob. Finally, according to the predicted poses, locate the LED chips precisely in the originally captured image based on gradient orientation features. Experiments show that the algorithm is not only robust to illumination variation but also can locate the LED chips and exclude the polycrystalline and fragmentary chips efficiently. [ABSTRACT FROM AUTHOR]