Cumulative degradation methodology to predict reliability of electronic systems

Purpose The purpose of this paper is to describe a novel methodology for predicting reliability for consumer electronics or any other hardware systems that experience a complex lifecycle environmental profile. Design/methodology/approach This Physics-of-Failure–based three-step methodology can be used to predict the degradation rate of a population using a Monte Carlo approach. The three steps include: using an empirical equation describing the degradation of a performance metric, a degradation consistency condition and a technique to account for cumulative degradation across multiple life-cycle stress conditions (e.g. temperature, voltage, mechanical load, etc.). Findings Two case studies are provided to illustrate the methodology including one related to repeated touch-load induced artifacts for displays. Originality/value This novel methodology can be applied to a wide range of applications from mechanical systems to electrical circuits. The results can be fed into the several stages of engineering validation to speed up product qualification.

Physics of Failure (PoF) Based Lifetime Prediction of Power Electronics at the Printed Circuit Board Level

This paper presents the use of physics of failure (PoF) methodology to infer fast and accurate lifetime predictions for power electronics at the printed circuit board (PCB) level in early design stages. It is shown that the ability to accurately model silicon–metal layers, semiconductor packaging, printed circuit boards (PCBs), and assemblies allows, for instance, the prediction of solder fatigue failure due to thermal, mechanical, and manufacturing conditions. The technique allows a life-cycle prognosis of the PCB, taking into account the environmental stresses it will encounter during the period of operation. Primarily, it involves converting an electronic computer aided design (eCAD) circuit layout into computational fluid dynamic (CFD) and finite element analysis (FEA) models with accurate geometries. From this, stressors, such as thermal cycling, mechanical shock, natural frequency, and harmonic and random vibrations, are applied to understand PCB degradation, and semiconductor and capacitor wear, and accordingly provide a method for high-fidelity power PCB modelling, which can be subsequently used to facilitate virtual testing and digital twinning for aircraft systems and sub-systems.

A combined universal generating function and physics of failure Reliability Prediction Method for an LED driver

The accurate and effective reliability prediction of light emitting diode (LED) drivers has emerged as a key issue in LED applications. However, previous studies have mainly focused on the reliability of electrolytic capacitors or other single components while ignoring circuit topology. In this study, universal generating function (UGF) and physics of failure (PoF) are integrated to predict the reliability of LED drivers. Utilizing PoF, lifetime data for each component are obtained. A system reliability model with multi-phase is established, and system reliability can be predicted using UGF. Illustrated by a two-channel LED driver, the beneficial effects of capacitors and MOSFETs for the reliability of LED drivers is verified. This study (i) provides a universal numerical approach to predict the lifetime of LED drivers considering circuit topology, (ii) enhances the modelling and reliability evaluation of circuits, and (iii) bridges the gap between component and circuit system levels.