Papers by Author: Chia Wei Chang

Abstract: The wafer defect inspection is an important process before die packaging. The defective regions were usually identified through visual judgment with the aid of a scanning electron microscope. Dozens of people visually check dies and mark their regions manually. Thus, potential misjudgment may be introduced due to human fatigue. In addition, the process can incur significant personnel costs. Self-Organizing Neural Networks (SONNs) have been proven to have the capabilities of unsupervised auto-clustering. In this paper, a hierarchical self-organizing neural networks (HSONN) with consideration of multiple textural information is proposed to replace the traditional electrical testing and the human inspection processes. The proposed HSONN is consists of 3 layers, each layer is a specific cluster to separate the block of wafer image into normal and defective regions. The suspicious defective regions are used as inputs to the next layer of HSONN for subsequent classification of the defective regions. Through the hierarchical classify, the output of the 3th layer denotes the detecting defective regions. Based on real-world data, the experimental results show that the proposed method successfully identifies the defective regions on wafers with good performances.

Abstract: The compensation of thermal deformation is the most significant for the accuracy of a machine tool. This study proposes an approach based on genetic algorithms (GA) to build the dynamic model of the prediction for thermal deformation of a machine tool. GA is used to optimize the prediction accuracy by using appropriate number and locations of temperature sensors, the model order and the time delay between temperatures and thermal deformation. The compared results show that the proposed approach can improve the accuracy of prediction results and better than other methods.