Predicting Luminance Decay of a Micro-LED Display via Machine Learning on Temperature Distribution and LED Degradation With Implementation by FPGA

Abstract A new method for predicting the luminance decay of Micro Light Emitting Diode (Micro-LED) displays by machine learning models is proposed herein with experiments of temperature distribution and degradation established. Although Micro-LEDs can be used as a direct light source for large outdoor advertising billboards, harsh outdoor conditions may lead to the degradation of Micro-LED displays. As a result, a temperature model is first built to predict the temperature distribution for the surface of a Micro-LED display based on illuminated patterns and the temperature sensors installed on the back of the display, followed by the establishment of degradation model for predicting luminance decay of the display based on Micro-LED enclosure temperature, input current, and illumination time. In addition to the establishment of those models, the implementation integrating two models in hardware is done with Verilog and verified by Xilinx Artix-7. The temperature model owns a prediction error of less than 1.1°C in various tests, while the degradation model has an average error of 1.05% (roughly 9 nits) for green light. The operating frequency for implementation can reach 76.92 MHz.