barcode scanner input asp.net RELIABILITY OF PRINTED CIRCUIT ASSEMBLIES in Software

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RELIABILITY OF PRINTED CIRCUIT ASSEMBLIES
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FIGURE 57.2 Classic bathtub reliability curve showing the three stages during the life of a product from a reliability perspective: infant mortality, steady-state, and wear-out.
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During the operating life, failures occur apparently randomly and the failure rate r is roughly constant with time. An exponential life distribution is often assumed to describe the behavior in this region. In that case, r= and R(t) = e rt = e MTBF where Nt = number of failures in time interval t No = number of samples at the beginning of the interval MTBF = mean time between failures
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During the third phase, the wear-out period, the failure rate increases gradually due to wearout phenomena until 100 percent of the units have failed. For some systems, the second steady-state region may not exist; for solder joints, the wear-out region may extend over most of the life of the assembly. Understanding wear-out phenomena, which manifest themselves in properly manufactured parts after a period of service, and predicting when they will significantly affect the failure rate, are the primary focuses of this chapter. Most wear-out phenomena can be characterized by cumulative failure distributions governed by either the Weibull or the log-normal distribution. Weibull distributions have been successfully used to describe solder joint and plated-through-hole fatigue distributions, while log-normal distributions are generally associated with electrochemical failure mechanisms. While these distributions may be quite narrow in some cases, their use should serve as a reminder that even with nominally identical samples, failures will be statistically distributed over time. A practical use of fitting a distribution to reliability data is to extrapolate to smaller failure rates or other environmental conditions. To simplify the equations, the expressions in the text refer to the mean life of the relevant portion of the assembly. If the constants that define the failure distribution are known, the time to reach a smaller proportion of failures may be readily calculated. For example, for failure modes that are described by a Weibull distribution, the time t to reach x% failures is given by: t(x%) = t(50%) ln(1 0.01x) ln (0.5)
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where is the Weibull shape parameter, usually between 2 and 4 for solder joint failures.
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Reliability Testing Almost every reliability test program must solve the problem of determining whether an object is reliable in a calendar time period that is much shorter than the expected use period. Obviously, one cannot spend 3 to 5 years testing a personal computer that will be marketed for an even shorter time span or 20 years testing a military system. Depending on the failure mechanism, there are two approaches, which may be combined: (1) accelerate the frequency of the occurrence that causes failure and test the ability of the object to survive the expected number of events, or (2) increase the severity so that fewer occurrences are needed. Drop tests that simulate shock during transportation are an example of the first approach. Since the time between drops does not affect the amount of damage caused, a lifetime of drops can be conducted in rapid succession. However, the effect of temperature and humidity on corrosion over the lifetime of the product can be tested only by increasing the temperature, the humidity, or the concentration of contaminants, or some combination of these. The difficulty is ensuring that the test reproduces and/or correlates to the failure mechanism in service. To use this data for making true reliability predictions that is, the probability of failures at a given time under given conditions testing must be continued until enough parts fail that a life distribution can be estimated. Unfortunately, this process can be time consuming and qualification tests are often substituted. Qualification test protocols specify a maximum number of failures that may be observed in a specified period in a sample of specified size. If few or no failures occur, a qualification test provides almost no information about the failure distribution; for example, the probability of failure during the next time interval is unknown. This limitation of qualification testing is minimized when the life distribution for properly manufactured samples is already known or can be estimated based on experience with similar designs. Many of the reliability tests described in Sec. 57.6 are actually qualification tests. Many reliability or qualification testing schedules follow neither of these schemes. Instead, they test the ability of the product to survive a sequence of tests under extremely severe conditions for a short time or small number of exposures. Again, this type of testing may be adequate when it is supported by long experience with both the product type and its use environment; however, it is risky because it is not based on ensuring that probable failure modes will not occur in the life of the product. When new technologies or geometries are introduced, the old tests may not always be conservative. By the same token, irrelevant failure modes that would not occur in service may be introduced by the harsh test conditions.
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