Semiconductor manufacturing industry is moving into the production of 300-mm wafers. To solve the increased workload problem in manual wafer handling, some personal guided vehicles (PGVs) have been developed to help in the transfer of front opening unified pods (FOUP). This study compares two kinds of PGVs with a traditional cart and evaluates the feasibility of using them for manual FOUP handling tasks. Manual FOUP handling capability was assessed. The results indicate that there is no obvious advantage in using any of the two evaluated PGV's over the manual cart. There is potential risk of causing musculoskeletal disorders for female operators to handle the 300-mm FOUP manually. Since the development of a fully automated intrabay FOUP handling system is a project of high technical difficulty, a combination of manual and automated handling is the current approach. To enhance the operator's health, safety and productivity, selection and training of operators, adequate design of handling tools and machine interface, assessment and balancing of workload are necessary.
This paper presents a systematic method to detect feature points on the silhouette of human body from front and side images. Based on the correspondence between the coordinates and the eight numbering system of chain codes, the codes that depicted at 90 degree angle were taken as corner points. The system then connects the other associated points horizontally and vertically. Thus, the human landmark positions of head, neck, shoulder, chest, waist, hip, thigh, knee, shank, ankle, and foot can be identified by the relationship between two correlative points. A total of 55 feature points and 26 body measurements can be extracted automatically. The method has been tested on four human subjects and all were correctly extracted. Keywords -Silhouette detection, human feature extraction, 2D image-based measurementsProceedings of the 2008 IEEE IEEM
The yield of semiconductor manufacturing can be improved through a learning process. A learning model is usually used to describe the learning process and to predict future yields. However, in traditional learning models such as Gruber's general yield model, the uncertainty and variation inherent in the learning process are not easy to consider. Also there are many strict assumptions about parameter distributions that need to be made. These result in the unreliability and imprecision of yield prediction. To improve the reliability and precision of yield prediction, expert opinions are consulted to evaluate and modify the learning model in this study. The fuzzy set theory is applied to facilitate this consulting process. At first, fuzzy forecasts are generated to predict future yields. The necessity of specifying strict parameter distributions is thus relaxed. Fuzzy yield forecasts can be defuzzified, or their -cuts can be considered in capacity planning. The interpretation of such a treatment is also intuitive. Then, experts are requested to evaluate the learning model and express their opinions about the parameters in suitable fuzzy numbers or linguistic terms defined in advance. Two correction functions are designed to incorporate expert opinions in the learning model. Some examples are used for demonstration. The advantages of the proposed method are then discussed.
With the development of aerospace technology, obtaining high-quality and high-precision imaging in the space environment puts forward higher requirements for the automatic exposure system of the terrain camera.The traditional automatic exposure algorithm will have obvious defects such as image quality degradation under complex lighting conditions, especially in the environment of backlight and uneven lighting distribution. Based on the automatic exposure system of FPGA, a fast automatic exposure algorithm based on variable step size is proposed.Firstly, the integration time is initially adjusted with a variable step size by using the weight mean value, and then the integration time is further adjusted with a fixed step size according to the gray histogram. The experimental results show that it can be accurately exposed under normal lighting and special lighting such as backlight, and the exposure time and image information entropy are improved compared with the traditional exposure algorithm.This method is suitable for special lighting conditions and can meet the imaging requirements of terrain cameras.
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