Sensitivity analysis of simplified Printed Circuit Board finite element models

• Published in: Microelectronics and reliability Vol. 49; no. 7; pp. 791 - 799
• Main Authors: Amy, Robin Alastair, Aglietti, G.S., Richardson, Guy
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-07-2009; Elsevier
• Subjects: Applied sciences; Electronic equipment and fabrication. Passive components, printed wiring boards, connectics; Electronics; Exact sciences and technology; Electronic equipment; Sensitivity analysis; Printed circuit board; Durability; Driver; Numerical method; Failures; Modeling; Finite element method; Electronic component; Printed circuit; Reliability; Damaging
• ISSN: 0026-2714; 1872-941X
• DOI: 10.1016/j.microrel.2009.04.002

Many items of electronic equipment are subjected to harsh vibrations during their lifetime, these vibrations can damage electronic components and potentially risk total device failure. One approach to assess this risk is to compare the predicted vibration response of the Printed Circuit Board against a vibration level that is experimentally determined to produce component failure. Theoretically the vibration response can be determined using a simplified model of the PCB, where the components are modelled using a “smeared” approach; however, the error due to using such a simplified approach has not yet been defined. This paper shows a process to calculate the errors produced by such simplification techniques and derives factors of safety that can be used for all future vibration response models, using these factors ensures that future predictions do not underestimate the real response. Additionally, the errors depends on several other values besides the simplification technique, namely the Printed Circuit Board properties and the component: type, location and density. To account for these factors the process will use a sensitivity analysis approach to consider many possible design cases, this approach involves the creation of a large number of randomly created cases, all with different input values and giving different factors of safety. In this way the statistics of the factor of safety can be built up, giving much greater confidence in the results and insight into the drivers of the modelling error.