Climatically Accelerated Material Processes Determining the Long-Term Reliability of Light-Emitting Diodes

Materials (Basel). 2024 Apr 3;17(7):1643. doi: 10.3390/ma17071643.

Abstract

LEDs (Light-Emitting Diodes) are widely applied not only in decorative illumination but also in everyday lighting in buildings, flats, public areas, and automotive fields. These application areas often mean harsh environments, for example, regarding the humidity content of the surrounding air: besides outdoor and automotive illumination, even the household use cases (kitchen, bathroom, cellar) may represent extreme temperature and humidity variations (often reaching relative humidity levels close to 100%) for these devices; thus, their reliability behaviour in such circumstances should be better understood. Thermally activated processes were studied in several previous publications, but less information is available regarding high-humidity environmental tests. Moisture and temperature ageing tests with appropriate environmental parameter settings were performed as accelerated lifetime tests to investigate not only the effect of temperature but also that of humidity on the ageing and reliability of LED packages containing RGB (red green blue) chips and phosphor-converted white (pcW) LEDs. The ageing was followed not only through monitoring optical/electrical/spectral parameters but also with material analysis. Moisture-material interaction models were proposed and set up. It was found that humidity-accelerated ageing processes are more severe than expected from previous assumptions. RGB and pcW LEDs showed strongly different behaviour.

Keywords: LEDs’ reliability and lifetime; ageing of LEDs; humidity accelerated processes in LEDs; spectral and luminous changes of LEDs.

Grants and funding

The work presented has received funding from the European Union’s Horizon 2020 research and innovation programme through the H2020 ECSEL project AI-TWILIGHT (grant agreement number: 101007319). Co-financing of the AI-TWILIGHT project. Co-financing of the AI-TWILIGHT project by the Hungarian government through the 2019-2.1.3-NEMZ_ECSEL-2021-00008 grant of the National Research, Development and Innovation Fund is also acknowledged.