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Microelectromechanical systems (MEMS) are a fast-growing field in microelectronics. MEMS are commonly used as actuators and sensors with a wide variety of applications in health care, automotives, and the military. The MEMS production cycle can be classified as three basic steps: (1) design process, (2) manufacturing process, and (3) operating cycle. Several studies have been conducted for steps (1) and (2); however, information regarding operational failure modes in MEMS is lacking. This paper discusses reliability in the context of MEMS functionality. It also presents a brief review of the most relevant failure mechanisms for MEMS.

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One responsibility of the reliability engineer is to monitor failure trends for fielded units to confirm that pre-production life testing results remain valid. This research suggests an approach that is computationally simple and can be used with a small number of failures per observation period. The approach is based on converting failure time data from fielded units to normal distribution data, using simple logarithmic or power transformations. Appropriate normalizing transformations for the classic life distributions (exponential, lognormal, and Weibull) are identified from the literature. Samples of size 500 field failure times are generated for seven different lifetime distributions (normal, lognormal, exponential, and four Weibulls of various shapes). Various control charts are then tested under three sampling schemes (individual, fixed, and random) and three system reliability degradations (large step, small step, and linear decrease in mean time between failures (MTBF)). The results of these tests are converted to performance measures of time to first out-of-control signal and persistence of signal after out-of-control status begins. Three of the wellknown Western Electric sensitizing rules are used to recognize the assignable cause signals. Based on this testing, theX-chart with fixed sample size is the best overall for field failure monitoring, although the individual chart was better for the transformed exponential and another highly-skewed Weibull. As expected, the linear decrease in MTBF is the most difficult change for any of the charts to detect.

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