Deep machine learning of the spectral power distribution of the LED system with multiple degradation mechanisms

Abstract

Abstract The performance and reliability of the light-emitting diode (LED) system significantly depend on the thermal–mechanical loading-enhanced multiple degradation mechanisms and their interactions. The complexity of the LED system restricts the theoretical understanding of the root causes of the luminous fluctuation or the establishment of the direct correlation between the thermal aging loading and the luminous outputs. This paper applies the deep machine learning techniques and develops a gated network with the two-step learning algorithm to build the empirical relationship between the design parameters and the thermal aging loading and the luminous output of LED products. The flexibility of the proposed method will be demonstrated by integrating it with different neural network architectures. The proposed gated network concept has been validated in both multiple LED chip packaging and LED luminaire under thermal aging loading. The validation of the luminous data of multiple LED chip packaging shows that the maximum differences of the correlated color temperature (CCT) and color coordinate are 2.6% and 1.0%, respectively. Moreover, the machine learning results of the LED luminaire exhibit that the differences of lumen depreciation, CCT and color coordinate are 1.6%, 1.9% and 1.1%, after 2160 h of thermal aging.