## 1 Introduction  
Advancement in electrotechnical devices has indeed revolutionize our daily lives. It will be hard to imagine going through our daily lives without cell phones, computers, and internets, to name a few. Consequently, our reliance on electrotechnical devices has been increasing significantly, and thus the dependability of these devices is becoming crucial. Dependability of a device is determined by its reliability and its constituting components therein. Reliability is defined as the probability that a product, system, or service will perform its intended function adequately for a specified period of time or will operate in a defined environment without failure. The component of reliability is as depicted in Figure 1 to the following: The components of reliability include probability, durability, dependability, availability and quality over time [1]. Although reliability is part of quality as can be learnt from the definition of quality according to ISO8402, which stated that quality is the totality of features and characteristics of a product or service that bear on its ability to satisfy stated or implied needs [2]. These stated or implied needs of course include the operation of products at prolonged period or its mission lifetime. However, due to the impossibility to check the reliability of every single product, as reliability tests usually involve time-consuming and destructive measurements, quality assurance can only assure the quality practice of the incoming materials, manufacturing processes through SPC, Cpk and yield, and product outcoming quality check against specifications. This renders a risk in the reliability of the products as depicted in Figure 1.  
Figure 1: Different between Reliability and Current Quality Practice. Figure 1 shows the strength distributions of two product batches, A and B. As the strength of the products in batch A is well above the specification, its quality at time zero is consider excellent whilst products in batch B suffers some yield loss. In both cases, their strengths are above the expected load to be applied to them, and hence they can operate without failure. During continuous operations, the strengths of products will degrade as expected. Thus, their mean strengths will be reduced, but the spread of the strength distribution increases since not all the products in a batch will degrade equally. If the degradation rate of products in batch A is much higher than that of batch B, some products in batch A will go below the load at time t', and some failure will occur. Such failures are considered as reliability failure. On the other hand, there is no failure for products in batch B, although its yield is not 100%. This figure explains the limitations of the current quality assurance methodology as degradation rate can only be found through reliability or accelerated life test. Even if reliability test is performed but with no failure, degradation rate will not be able to determine either. However, the determination of the degradation rate can be time consuming and cost intensive as the number of failures in a selected accelerated life test must be sufficient to provide a good estimation of the degradation rate of a batch. Furthermore, the choice of accelerated life test must correspond to the dominant failure mechanisms of the products in normal field operating conditions, and this requires detail failure analysis of field failure if the product is already in the market or a good understanding of the possible various failure mechanisms before the products are launched. To address the above-mentioned, and in view of the short time to market available for products, a new research area has recently emerged where the physics of failures and material sciences are employed to explore the various possible degradation mechanisms with the aid of powerful simulation tools.