Prof. Chien-Chih Wang
Industrial Engineering and Management, Ming Chi University of Technology, Taipei, Taiwan
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Academic Career Length
25Years (2002-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 November 2002 in IEEE Transactions on Semiconductor Manufacturing
Journal Article
In the semiconductor industry, IC marking error remains a problem. The objective of this study is to identify IC marking using gray relational analysis. The gray theorem determines the gray relational grades of all of the selected factors by choosing the highest gray relational grade, even under incomplete information circumstances. In an IC marking identification procedure, an image is rotated and segmented first. Second, thresholding and thinning operations are applied to reduce the calculation complexity and extract features from the segmented image. Finally, the gray relational analysis method is applied to inspect the IC markings. The identification rate reaches 97.5%. As compared to traditional methods, there are three advantages in gray relational analysis: 1) No large amount of data is needed; 2) No specific statistical data distribution is required; and 3) There is no requirement for the independency of the factors to be considered. It is an easy and practical method in the field of IC marking inspection.
1 January 2005 in International Journal of Production Research
Journal Article
Display quality is part of the final liquid crystal display (LCD) inspection process before shipping. A ‘limited sample’ is provided based on the agreement between the manufacturer and the customer. This inspection usually includes an operator who compares the LCD product with the limit sample using the naked eye. The procedure often causes controversy in the manufacturing plant and between the manufacturer and customers. This study attempts to establish a more objective, automatic method to determine the MURA-type defects in LCD panels. A luminance meter is used as the measurement device. An LCD panel is divided into 144 areas. Five points are measured to obtain the luminances. Analysis of variance and the exponentially weighted moving average techniques are applied to determine the existence of MURA defects. Fifty normal LCD panels and 50 MURA defects panels were used to test the inspection method. All 50 LCD panels with MURA defects were correctly identified using the proposed inspection method. The proposed inspection method can help LCD manufacturers reduce the variation in LCD panel inspection results and establish a better relationship with customers through a common inspection mechanism.
Journal Article
Suppliers are adjusting from the order-to-order manufacturing production mode toward demand forecasting. In the meantime, customers have increased demand uncertainty due to their own considerations, such as end-product demand frustration, which leads to suppliers’ inaccurate demand forecasting and inventory wastes. Our research applies ARIMA and LSTM techniques to establish rolling forecast models, which greatly improve accuracy and efficiency of demand and inventory forecasting. The forecast models, developed through historical data, are evaluated and verified by the root mean squares and average absolute error percentages in the actual case application, i.e., the orders of IC trays for semiconductor production plants. The proposed ARIMA and LSTM are superior to the manufacturer’s empirical model prediction results, with LSTM exhibiting enhanced performance in terms of short-term forecasting. The inventory continued to decline significantly after two months of model implementation and application.
1 May 2011 in Expert Systems with Applications
Journal Article
1 August 2012 in Expert Systems with Applications
Journal Article
CJChih-Hung Jen
Chih-Hung Jen
Chien-Chih WangBJBernard C. Jiang
Bernard C. Jiang
YCYan-Hua Chu
Yan-Hua Chu
Ming-Shu Chen3 May 2013 in IEEE Transactions on Semiconductor Manufacturing
Journal Article
Chien-Chih WangBJBernard C. Jiang
Bernard C. Jiang
JLJing-You Lin
Jing-You Lin
CCChien-Cheng Chu
Chien-Cheng Chu
The normalized cross correlation coefficient is a prevalent pattern-matching algorithm in machine vision for industrial inspections. Despite its common use, there are problems with practical applications. For instance, false alarms occur since it is highly sensitive to environmental changes or inspection equipment, not to mention it requires complex calculations. This paper proposes the partial information correlation coefficient (PICC) method to improve the traditional normalized cross correlation coefficient (TNCCC). The PICC uses the technique of significant points to calculate the correlation coefficient. An experiment is also conducted to demonstrate the application through many image samples from the IC industry, such as PCBs, BGAs, and ICs. The results show that the PICC can effectively reduce false alarms in defect detection.
About Me
Dr. Chien-Chih Wang is a Professor in the Department of Industrial Engineering and Management at Ming Chi University of Technology (MCUT), Taiwan. With a strong interdisciplinary background, he received his B.S. in Applied Mathematics from National Chung Hsing University (1992), M.S. in Statistics from National Cheng Kung University (1994), and Ph.D. in Industrial Engineering and Management from Yuan Ze University (2001). Dr. Wang specializes in data analytics and intelligent decision-making, with expertise spanning process quality optimization, Lean Six Sigma, machine vision inspection, and decision support systems in healthcare. He has secured research grants from the National Science and Technology Council (NSTC) and engaged in numerous industry–academia collaborative projects. To date, Dr. Wang has published over 80 peer-reviewed journal articles and led more than 70 industrial–academic cooperation projects. His work emphasizes bridging theory and practice, particularly in applying AI and statistical methods to real-world manufacturing and service systems. He actively contributes to professional communities, including the Chinese Institute of Industrial Engineers (CIIE), Chinese Society for Quality (CSQ), Electronic Business Management Society (EBMS), and Taiwan Reliability Technology Association (TRTA).