Traditional statistical process control (SPC) techniques applied in the industrial processes field consider often that the distribution ofdata is Gaussian. The estimation ofparameters, the detection ofthe out oforder situations and the control ofthe followed characteristics are easy to achieve for the normal populations. In reality, whatever the origin of a characteristic (large series productions for components, mechanical parts of OE communication systems, etc. ) the curve of distributions of the measured values is generally far from being normal. The simple approximation to the Gauss distribution and the use ofthe classical control methods sometimes induces serious errors. In this paper, a study on the statistical control of non Gaussian populations is presented. Particularly we discuss the Rayleigh and the Weibull distribution as being representatives in (SPC for some category of data. The X control charts with variable limits are tested. Experimental simulations are presented for different parameters of the two distributions. The results confirm the methodology and encourage the research in the field of non Gaussian processes.
With the globalization of the markets and the growth of competitiveness in the manufacturing sector, quality has become a key factor of success. Quality is particularly important for the companies which activate in the micro(electronics) field. The quality management system holds a vital place in the company's structure. Implementing such a system requires important operating costs. These costs are known as Quality Obtaining Costs (QOC) and may be considered as an investment. Planning an investment, means evaluating its return in order to see if it is profitable or not. Measuring the return of quality politics investment raise some delicate problems. We may calculate some aspects of the return of investment by measuring the shape of non-quality costs. An eventual decrease of these costs could be synonym with a profitable investment. But the advantages of good quality politics cannot be measured only by taking into consideration the non-quality costs (even if they include direct and indirect costs). There are also intangible advantages (like mark image, competences, polyvalence, client's satisfaction…) that derive from quality approaches. How to evaluate this type of consequences / advantages? The idea developed in this article is to considerate the quality politics like un immaterial/intelligent investment. Therefore could it be advantageous / possible to use the immaterial investment's measuring and evaluation techniques for studying the quality politics return of investment?Quality, quality management, quality cost, quality politics, QOC, microelectronics
Although higher reliability is expected from submicron and nanotechnology so far only a few attempts have been made to apply reliability theory to submicron and nanodevices. The way to reliable rianotechnology is to identify relevant physical failure mechanisms and corresponding failure rates, determine reliability indices, and investigate reliability models down to nanoseale including quantum processes. Perhaps the most significant problem concerns the sensitivity of future IC generations face to various noise sources, and in particularly face to energetic particles. This paper analyses some of the above problems. At the same time, we propose the implementation of a new soft error detecting technique based on time redundancy.
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