Predicting Luminance Decay of a Micro-LED display via Machine Learnings on Temperature Distribution and LED Degradation

Predicting Luminance Decay of a Micro-LED display via Machine Learnings on Temperature Distribution and LED Degradation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting Luminance Decay of a Micro-LED display via Machine Learnings on Temperature Distribution and LED Degradation Paul C.-P. Chao, Chi-En Lin, Hao-Ren Chen, Duc Huy Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4148384/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Nov, 2024 Read the published version in Microsystem Technologies → Version 1 posted 10 You are reading this latest preprint version 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 models for predicting luminance decay of the display based on Micro-LED enclosure temperature, input current, and illumination time. Based on the different degradation characteristics observed for red, green, and blue light in the experiments, their degradation models are established separately. In addition, exponential curve-fitting and interpolation are conducted based on TM-21 for the high accuracy when predicting for a much longer aging period. The temperature model built exhibits a prediction error of less than 1.1°C, while an average error is kept below 1.05% (roughly 9 nits). Moreover, the predicted period using the proposed degradation model with high accuracy can reach up to tens of thousands or even hundreds of thousands of hours. It is evident that the predicted results within this long period meets the requirement of the exponential curve defined by TM-21. Micro-LED luminance degradation neural network (NN) temperature distribution hardware implementation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Nov, 2024 Read the published version in Microsystem Technologies → Version 1 posted Editorial decision: Revision requested 20 Sep, 2024 Reviews received at journal 20 Sep, 2024 Reviewers agreed at journal 20 Sep, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers agreed at journal 04 Apr, 2024 Reviewers agreed at journal 02 Apr, 2024 Reviewers invited by journal 02 Apr, 2024 Editor assigned by journal 27 Mar, 2024 Submission checks completed at journal 27 Mar, 2024 First submitted to journal 22 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4148384","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285135232,"identity":"0cd96e14-be6d-4fbe-85b2-2021a42743c0","order_by":0,"name":"Paul C.-P. 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