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A semiconductor manufacturer conducted a reliability test to determine the effect of thermal shock cycle on solder joint life (I.e., one thermal cycle is equivalent to how much operation time). This presentation describes the scenario and shows how Life Data Analysis is used to solve this problem. This article also introduces BX-Life (a reliability matrix) and Confidence Bounds (a concept to quantify uncertainty) in Life-Data Analysis.

The requirements for higher reliability have increased the need for more up-front testing of materials, components, and systems. Accelerated test have become increasingly important because of rapidly changing technologies, more complicated products with more components, higher customer expectations for better reliability, and the need for rapid product development.

Manufactured products are tested at much higher constant usage rate so that reliability test can be completed in shorter time. However, the failure distribution obtained from the lab test cannot be used for warranty return analysis because the products experience very much different usage stress in the fields. This article presents a simulation approach to estimate product field-return distribution, given the failure distribution obtained from lab-test, and customer usage profile/distribution.

This presentation demonstrates how Life Data Analysis is used to compare the reliability of a component from two suppliers (the concept can also apply to comparing different designs). It also include a cost-benefit analysis that illustrates the financial impact due to warranty cost associated to the choice of supplier.

Consider the following scenarios: An iron ore receiving port takes in shipments and keep it in a holding area. Meanwhile, the ore is consumed by nearby processing plant. In this article, I will discuss about how to analyze production impact due to supply (or input) variation. In Operation Research, this type of problem is categorized under Queueing Theory.

In Printed Circuit Board Assemblies, solder joint failures can occur due to fatigue caused by temperature cycling. Norris-Landzberg model has been used to model fatigue failure in solder joints due to repeated temperature cycling as the device is switched on and off.

Norris-Landzberg model provides a basis for estimating the field life once the model parameters are determined.

In this article, we analyze accelerated test data using the Norris-Landzberg model for lead-free solder joint low-cycle fatigue, and solve for its model parameters by using the general log-linear (GLL) model.

When a manufacturer has the luxury to make a large profit from its products, there is less incentive for the manufacturer to analyze the warranty cost. As the manufacturing cost increases over time, and selling price decreases due to competition, the impact of warranty cost on the profit margin become more pronounced.

A manufacturer has reliability program in place. They have a very good understanding on the failure modes (in terms of its failure rate behavior) of its particular product. The manufacturer wants to know what is the cost benefit, specifically warranty cost reduction, of a reliability improvement program, and hence decide whether to proceed with the program.

In this case study we present an analysis for designing a chemical plant process, part of an extension project and during the FEED stage. The method and approach presented (as well as the software used – AeROS) is equally applicable in other industries where buffer sizing is necessary (such as Oil & Gas, manufacturing, etc.). A key takeaway from this study, is the importance of considering equipment reliability, repair durations, and production, and their impact on the overall process, when making design decisions.