Micro-LED backlight module by deep reinforcement learning and micro-macro-hybrid environment control agent

Che Hsuan Huang, Yu Tang Cheng, Yung Chi Tsao, Xinke Liu, Hao Chung Kuo

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This paper proposes a micro-LED backlight module with a distributed Bragg reflector (DBR) structure to achieve excellent micro-LED backlight module quality and uses deep reinforcement learning (DRL) architecture for optical design. In the DRL architecture, to solve the computing environment problems of the two extreme structures of micro-scale and macro-scale, this paper proposes an environment control agent and virtual-realistic workflow to ensure that the design environment parameters are highly correlated with experimental results. This paper successfully designed a micro-LED backlight module with a DBR structure by the abovementioned methods. The micro-LED backlight module with a DBR structure improves the uniformity performance by 32% compared with the micro-LED backlight module without DBR, and the design calculation time required by the DRL method is only 17.9% of the traditional optical simulation.

Original languageEnglish
Pages (from-to)269-279
Number of pages11
JournalPhotonics Research
Volume10
Issue number2
DOIs
StatePublished - 1 Feb 2022

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