A Gamma process-based degradation testing of silicone encapsulant used in LED packaging. (April 2021)

• Record Type: Journal Article
• Authors: Fan, Jiajie; Chen, Ye; Jing, Zhou; Ibrahim, Mesfin Seid; Cai, Miao
• Is Part Of: Polymer testing. Volume 96(2021)
• Publication Date: 2021-04
• Subjects: LED packaging -- Silicone encapsulant -- Lumen decay -- Color shift -- Gamma process; Polymers -- Testing -- Periodicals; Polymères -- Tests -- Périodiques; 620.1920287
• DOI: 10.1016/j.polymertesting.2021.107090
• Languages: English
• ISSNs: 0142-9418

Abstract

Abstract: Silicone encapsulant is widely used in light-emitting diode (LED) packaging because it offers high light transmittance, high refractive index, high thermal stability, and long lifetimes. However, it is extremely sensitive to moisture when the LED operates under high temperature and high humidity. In this study, constant moisture stress-accelerated degradation tests (CSADT) are designed with three different thermal stresses on silicone encapsulant, and its real lumen decay and color shift lifetimes are predicted by integrating the accelerated lifetime model with the Gamma process model considering the random effects of the degradation data. The results show that: (1) the lumen decay path after natural logarithmic transformation as well as the color shift degradation path can be well fitted with the linear model. In addition, the degradation rate is related to the thermal stress under the same humidity stress condition; (2) the Gamma process model has a high prediction accuracy when the lumen decay lifetime is used for estimation. On the contrary, the least squares regression (LSR) model has shown superior prediction accuracy versus the Gamma process model when color shift degradation data are used. Highlights: Constant moisture stress-accelerated degradation tests are designed on silicone encapsulant used in LED packaging. A Gamma process-based degradation testing method is proposed considering the random effects of the degradation data. Gamma process model has a high prediction accuracy in lumen decay lifetime estimation.