Machine learning based luminance analysis of a $\mu$LED array

TL;DR

This paper introduces an unsupervised machine learning method to efficiently analyze luminance and color in $ ext{LED}$ arrays, improving accuracy and reducing measurement time for micro-LED development.

Contribution

It presents a novel machine learning approach that enables simultaneous luminance and color analysis of $ ext{LED}$ arrays from a single measurement, addressing previous measurement challenges.

Findings

Enhanced reconstruction accuracy of $ ext{LED}$s

Reduced measurement and evaluation time

More precise characterization of $ ext{LED}$ arrays

Abstract

In the past years, the development of $\mu$LED arrays gained momentum since they combine the advantages of $\mu$LEDs, such as high brightness and longevity, with a high resolution of a micro-scaled structure. For the development, spatially resolved measurements of luminance and color of single $\mu$LEDs and the entire light-emitting surface are analyzed as they are crucial for the visual perception. However, the former is time intense in measurement and evaluation, and the latter suffers from interference caused by nonfunctional $\mu$LEDs. This paper presents a method to perform both analyzes with a single measurement using unsupervised machine learning. The results suggest that a precious reconstruction of the $\mu$LEDs and a more accurate characterization $\mu$LED arrays can be achieved.