A Comparative Analysis of Statistical Modeling and Machine Learning Techniques for Predicting the Lifetime of Light Emitting Diodes From Accelerated Life Testing

Abstract

This work uses multivariable life stress models to revisit the catastrophic failure of high-brightness blue light emitting diodes (LEDs) under accelerated life testing (ALT). The stress factors, current, temperature, relative humidity (RH), and their interactions are considered in lifetime studies. First, we show that the lognormal distribution fits the experimental data much better than the Weibull distribution using the standard Kolmogorov-Smirnov test. Furthermore, the best life-stress relationship is the Intel model rather than the peck model used by Nogueira et al. (2016). Additionally, based on the accelerated data, machine learning (ML) techniques are employed to predict the lifetime of LEDs under normal operating conditions. However, the study highlights the limitations of ML in accurately predicting lifetime.