Digital Luminaire Design Using LED Digital Twins—Accuracy and Reduced Computation Time: A Delphi4LED Methodology

Abstract

Light-emitting diode (LED) digital twins enable the implementation of fast digital design flows for LED-based products as the lighting industry moves towards Industry 4.0. The LED digital twin developed in the European project Delphi4LED mimics the thermal-electrical-optical behavior of a physical LED. It consists of two parts: a package-level LED compact thermal model (CTM), coupled to a chip-level multi-domain model. In this paper, the accuracy and computation time reductions achieved by using LED CTMs, compared to LED detailed thermal models, in 3D system-level models with a large number of LEDs are investigated. This is done up to luminaire-level, where all heat transfer mechanisms are accounted for, and up to 60 LEDs. First, we characterize a physical phosphor-converted white high-power LED and apply LED-level modelling to produce an LED detailed model and an LED CTM following the Delphi4LED methodology. It is shown that the steady-state junction temperature errors of the LED CTM, compared to the detailed model, are smaller than 2% on LED-level. To assess the accuracy and the reduction of computation time that can be realized in a 3D system-level model with a large number of LEDs, two use cases are considered: (1) an LED module-level model, and (2) an LED luminaire-level model. In the LED module-level model, the LED CTMs predict junction temperatures within about 6% of the LED detailed models, and reduce the calculation time by up to nearly a factor 13. In the LED luminaire-level model, the LED CTMs predict junctions temperatures within about 1% of LED detailed models and reduce the calculation time by about a factor of 4. This shows that the achievable computation time reduction depends on the complexity of the 3D model environment. Nevertheless, the results demonstrate that using LED CTMs has the potential to significantly decrease computation times in 3D system-level models with large numbers of LEDs, while maintaining junction temperature accuracy.

Efficiency Models for GaN-Based Light-Emitting Diodes: Status and Challenges

“…Bulk ternary layers suffer from strong alloy scattering of phonons ( Figure 11 ) [ 117 ]. LED chip mounting and packaging also influence the self-heating significantly [ 118 ]. Thus, there is much uncertainty about the thermal material properties of real devices so that thermal resistance measurements are often preferred over self-heating simulations.…”

“…In fact, the strength of machine learning lies in the analysis of large amounts of experimental data which are often routinely collected in the industrial LED production. The combination of reality-trained artificial neural networks (ANNs) with numerical simulations could lead to the creation of realistic digital twins that support the LED design and production process [ 118 , 131 , 132 ].…”

Fast Characterization of Power LEDs: Circuit Design and Experimental Results

“…The first main goal of the circuit proposed in this work is to allow the measurement of self-heating transients of optoelectronics devices. The measurement is based on the analysis of the voltage transients generated by a step in the LED current [17], [24], [25]. Assuming a 1-D heat flow, device self-heating leads to a decrease in the operating voltage, with several time constants which are related to the individual thermal interfaces within the LED structure.…”

A Digital Twin for a Chiral Sensing Platform

“…[5] A digital twin can be used to control and predict the performance of the real-world system based on data obtained from the system. In photonics and related fields, the concept has received attention in material processing, [6,7] imaging, [8] design of light-emitting diodes, [9,10] and design of optical measurement setups. [11] In this work, we address the problems of nanophotonicsenhanced spectroscopy experiments by introducing a digital twin that models the entire nanophotonic system and can act as an integral part of the experiment.…”

“…[ 5 ] A digital twin can be used to control and predict the performance of the real‐world system based on data obtained from the system. In photonics and related fields, the concept has received attention in material processing, [ 6,7 ] imaging, [ 8 ] design of light‐emitting diodes, [ 9,10 ] and design of optical measurement setups. [ 11 ]…”