Solid state lighting devices with high power densities require accurate characterization, as the rise of chip temperature impacts its optical, electrical, and lifetime characteristics. A detailed experimental study is performed in order to analyze opto-electro-thermal behavior as new high-power devices like laser diodes are becoming of interest. The outline of this article was inspired by two major issues identified in previous experimental approaches. First, there is some debate in the literature about the linearity of the temperature coefficient of the forward voltage. Second, there is a limited number of experimental reports on the temperature dependence of power conversion efficiency. We have shown that prior variations are the result of a diversity in the selection of electrical parameters during thermal calibrations, as both linear and non-linear relationships for temperature coefficient of voltage can be obtained. On the other hand, it was discovered that report scarcity for temperature-dependent studies can be related to the use of passive approaches for temperature-dependent measurements. In temperature-controlled environments, short pulses with high current densities may not ensure the thermal equilibrium of the device under test due to small thermal capacitance. This issue, as well as the lengthy process of passive measurements can be addressed by implementing a dynamic measurement method presented in this study. Finally, linear power conversion efficiency trends with junction temperature are demonstrated for both blue LEDs and LDs at high current densities.

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In the new age of illumination, light emitting diodes (LEDs) have been proven to be the most efficient alternative to conventional light sources. Yet, in comparison to other lighting systems, LEDs operate at low temperatures while junction temperature (Tj) is is among the main factors dictating their lifespan, reliability, and performance. This indicates that accurate measurement of LED temperature is of great importance to better understand the thermal effects over a system and improve performance. Over the years, various Tj measurement techniques have been developed, and existing methods have been improved in many ways with technological and scientific advancements. Correspondingly, in order to address the governing phenomena, benefits, drawbacks, possibilities, and applications, a wide range of measurement techniques and systems are covered. This paper comprises a large number of published studies on junction temperature measurement approaches for LEDs, and a summary of the experimental parameters employed in the literature are given as a reference. In addition, some of the corrections noted in non-ideal thermal calibration processes are discussed and presented. Finally, a comparison between methods will provide the readers a better insight into the topic and direction for future research.

While junction temperature control is an indispensable part of having reliable solid-state lighting, there is no direct method to measure its quantity. Among various methods, temperature-sensitive optical parameter-based junction temperature measurement techniques have been used in practice. Researchers calibrate different spectral power distribution behaviors to a specific temperature and then use that to predict the junction temperature. White light in white LEDs is composed of blue chip emission and down-converted emission from photoluminescent particles, each with its own behavior at different temperatures. These two emissions can be combined in an unlimited number of ways to produce diverse white colors at different brightness levels. The shape of the spectral power distribution can, in essence, be compressed into a correlated color temperature (CCT). The intensity level of the spectral power distribution can be inferred from the luminous flux as it is the special weighted integration of the spectral power distribution. This paper demonstrates that knowing the color characteristics and power level provide enough information for possible regressor trainings to predict any white LED junction temperature. A database from manufacturer datasheets is utilized to develop four machine learning-based models, viz., k-Nearest Neighbor (KNN), Radius Near Neighbors (RNN), Random Forest (RF), and Extreme Gradient Booster (XGB). The models were used to predict the junction temperatures from a set of dynamic opto-thermal measurements. This study shows that machine learning algorithms can be employed as reliable novel prediction tools for junction temperature estimation, particularly where measuring equipment limitations exist, as in wafer-level probing or phosphor-coated chips.

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