The unique radiative, photometric and colorimetric characteristic of a light-emitting diode is derived from its spectral power distribution. Modeling such characteristics with respect to the forward current, temperature or operating time has been subject of various studies. Deriving a simple analytical model, however, is not trivial due to the unique emission pattern varying with different types and technologies of light emitting diodes. For this purpose, curve fitting multiple superimposed Gaussian probability density functions to the spectral power distribution is a common approach. Despite excellent $R^{2}$ goodness of fit results, significant deviations within the photometric and colorimetric parameters, such as luminous flux or chromaticity coordinates, are observed. In addition, most studies were conducted on a small sample set of very few different spectral power distributions. This work provides a comprehensive comparison and evaluation of 19 different (superimposed) probability density function based models provided by the literature tested on a total of 15 different spectral power distributions of monochromatic blue, green and red light-emitting diode as well as phosphor-converted spectra of lime, purple and white samples with different correlated color temperatures. All models were evaluated by means of their coefficient of determination, radiant flux, chromaticity coordinate deviation and Bayesian Information Criterion. This study shows that a superimposed (split) Pearson VII model is able to outperform the commonly used Gaussian model approach by far. In addition, an application example in regard of forward current dependence is given to prove the proposed approach.

We report on the degradation mechanisms and dynamics of silicone encapsulated ultraviolet A (UV-A) high-power light-emitting diodes (LEDs), with a peak wavelength of ${\mathrm {365~ \text {n} \text {m} }}$ . The stress tests were carried out for a period of 8665 hours with forward currents between ${\mathrm {350~ \text {m} \text {A} }}$ and ${\mathrm {700~ \text {m} \text {A} }}$ and junction temperatures up to 132°C. Depending on stress condition, a significant decrease in optical power could be observed, being accelerated with higher operating conditions. Devices stressed at a case temperature of 55 °C indicate a decrease in radiant flux between 10- ${\mathrm {40~\%}}$ varying with measurement current, whereas samples stressed at higher case temperatures exhibit crack formation in the silicone encapsulant accompanied by electromigration shorting the active region. The analyzed current and temperature dependency of the degradation mechanisms allows to propose a degradation model to determine the device lifetime at different operating parameters. Additional stress test data collected at different aging conditions is used to validate the model’s lifetime predictions.

Modern indoor lighting faces the challenge of finding an appropriate balance between energy consumption, legal requirements, visual performance, and the circadian effectiveness of a spectrum. Multi-channel LED luminaires have the option of keeping image-forming metrics steady while varying the melanopic radiance through metamer spectra for non-visual purposes. Here, we propose the theoretical concept of an automated smart lighting system that is designed to satisfy the user’s visual preference through neural networks while triggering the non-visual pathway via metamers. To quantify the melanopic limits of metamers at a steady chromaticity point, we have used 561 chromaticity coordinates along the Planckian locus (2700 K to 7443 K, ±Duv 0 to 0.048) as optimisation targets and generated the spectra by using a 6-channel, 8-channel, and 11-channel LED combination at three different luminance levels. We have found that in a best-case scenario, the melanopic radiance can be varied up to 65% while keeping the chromaticity coordinates constant $\left(\Delta u^{\prime }{v}^{\prime }\le 7.05\times {10}^{-5}\right)$ by using metamer spectra. The highest melanopic metamer contrast can be reached near the Planckian locus between 3292 and 4717 K within a Duv range of −0.009 to 0.006. Additionally, we publish over 1.2 million optimised spectra generated by multichannel LED luminaires as an open-source dataset along with this work.

We report on the degradation dynamics and mechanisms of the commercially available chip-on-board (COB) high-power light-emitting diode (LED) modules with an electrical power of 175 W. Due to the associated thermal load, the temperature dependence of the aging processes is additionally analyzed within the scope of this work. The aging tests were performed for a period of 6000 h at four different case temperatures between 55 $^\circ$ C and 120 $^\circ$ C. The results of the accelerated stress tests indicate a temperature-activated aging process, which severely limits the lifetime of the modules. In addition, the following key findings can be reported: 1) a significant decrease in optical power occurs within 6000 h of operation; 2) depending on the stress test condition the accompanying color shifts exceed a limit of $\Delta \textit{u}\,'\textit{v}\,'=\text{0}.\text{007}$ ; and 3) the limiting degradation mechanism can be attributed to the package of the device and can be accelerated with temperature, current, and chemicals. Reported findings can be manifested by additional optical material inspections, allowing to use the results for optimizations of future module generations.

This study explores a novel approach to monitor the spectral emission of LEDs by estimating the spectral power distribution from the spectral sensor responses during an accelerated ageing experiment. Two methods for reconstructing the actual LED spectra from sensor responses are presented and tested, one solely requires sensor datasheet information and the other uses a full spectral characterisation of the sensor’s spectral sensitivities. The reconstruction results show that a spectral sensor can provide accurate spectral estimates even after severe LED degradation. Only for an LED that suffered a phosphor crack, affecting its spatial radiation characteristics, limited ability to estimate the true spectral power distribution without prior assumptions about the spectral changes must be reported. Overall, the use of a spectral sensor, even without detailed characterisation of the sensor itself, allows for an accurate monitoring of the true emission of LEDs, with a maximum radiometric error of 0.73 %, a maximum colormetric error of 0.0017Δ u ′ v ′ and a maximum spectral nRMSE error of 0.0097 compared to a spectroradiometric measurement. This advance holds great promise for improving lighting technology, particularly in applications that require constant radiometric output and stable color.

Precise determination of the photosynthetic photon flux density (PPFD) is important for illumination in modern horticultural systems. This paper presents two methods to calculate the PPFD of daylight spectra using low cost optic sensors. The first method uses the spectral sensitivity functions of spectral sensors to recreate the quantum sensitivity curve of quantum sensors. Two sets of spectral sensitivity functions are compared. The second method calculates the PPFD based on the calculated correlated colour temperature and a spectral reconstruction using the CIE daylight model. It is demonstrated that all methods offer a useful estimation of the PPFD with correspondingly similar daylight spectra, but the supposedly simpler method, which is based on weighting of the individual channels, is more stable against deviations from the CIE daylight model.