The introduction of high-power white LEDs has revolutionized the lighting industry in the past few decades due to the multiple benefits in terms of high reliability, environmental friendliness and versatile applications. However, challenges have arisen in assessing the reliability and lifetime prediction because it is difficult to record the failure data in a short period of time. Currently, the nonlinear least squares (NLS) regression-based method is used in industry for projecting the lumen maintenance lifetime from degradation data. The model parameters estimated using the NLS regression approach are deterministic and introduce high prediction errors. In this paper, a Bayesian method is proposed to estimate the remaining useful lifetimes (RULs) of both high-power white LED packages and lamps. The accelerated degradation tests conducted for gathering lumen degradation data are used to validate the proposed method. The exponential decay model is used as the degradation model and the parameters are estimated based on Markov Chain Monte Carlo (MCMC) sampling and using the Metropolis-Hasting (MH) algorithm. The lifetime prediction results showed that the Bayesian method has better prediction accuracy compared to the NLS method. Thus, the proposed Bayesian method is shown to be a promising approach to address the lifetime prediction issue for high-power white LEDs with improved prediction accuracy.

To make the light-emitting diode (LED) more compact and effective, the flip chip solder joint is recommended in LED chip-scale packaging (CSP) with critical functions in mechanical support, heat dissipation, and electrical conductivity. However, the generation of voids always challenges the mechanical strength, thermal stability, and reliability of solder joints. This paper models the 3D random voids generation in the LED flip chip Sn96.5-Ag3.0-Cu0.5 (SAC305) solder joint, and investigates the effect of thermal shock load on its mechanical reliability with both simulations and experiments referring to the JEDEC thermal shock test standard (JESD22-A106B). The results reveal the following: (1) the void rate of the solder joint increases after thermal shock ageing, and its shear strength exponentially degrades. (2) the first principal stress of the solder joint is not obviously increased, however, if the through-hole voids emerged in the corner of solder joints, it will dramatically increase. (3) modelling of the fatigue failure of solder joint with randomly distributed voids utilizes the approximate model to estimate the lifetime, and the experimental results confirm that the absolute prediction error can be controlled around 2.84%.

The estimation of lifetime for highly reliable products including Ultraviolet Light-emitting Diodes (UV LEDs) has been challenging based on traditional lifetime tests that records time to failure. Recently constant stress and step stress degradation tests are used to gather degradation path data and modeling the degradation of performance characteristics has been applied. In this paper, a step stress accelerated degradation test (SSADT) designed to capture the degradation path and study the lifetime of UV LEDs. The radiation power degradation path was analyzed based on the IES TM-21 least square regression (LSR) and Wiener process approach. With its advantage of requiring smaller sample size and shorter test time, the SSADT provides a degradation path suitable for the proposed Wiener process modeling. The lifetime estimation for UV LEDs based on the proposed wiener process approach shows better prediction accuracy compared to the TM-21 LSR approach. By using this method, dynamic changes and degradation of the UV LEDs can be easily studied, and it can effectively estimate their remaining useful lifetimes.

Nowadays, due to the advancement of design and manufacturing technology, there are many consumer products with high reliability. Similarly, the competition in the business sector influences the product development time to become shorter and that makes it difficult for manufacturers to evaluate the reliability of current products before new products are released to the market. This phenomenon is manifested in the lighting industry, especially for the high power white light-emitting diodes (LEDs) as these products have a long lifetime and high reliability. Currently, the standard to predict the lifetime of LEDs is based on a deterministic nonlinear least squares method which has low prediction accuracy. To overcome this, degradation models are being used to study the reliability of such products, considering the uncertainties and the quality characteristics whose degradation over a period of time can be related to the product lifetime. A stochastic approach based on gamma distributed degradation (GDD) is proposed in this study to estimate the long-term lumen degradation lifetime of phosphor-converted white LEDs. An accelerated thermal degradation test was designed to gather luminous flux degradation data which was analyzed based on maximum likelihood estimation (MLE) and the method of moments (MM) to estimate the parameters for the GDD model. The MLE method has shown superiority over MM in terms of the estimation of the model parameters due to its iterative algorithm being likely to find the optimal estimation. The lifetime prediction results show that the accuracy of the proposed method is much better than the TM-21 nonlinear least squares (NLS) approach which makes it promising for future industrial applications.