Optimized Design with Artificial Intelligence Quantum Dot White Mini LED Backlight Module Development

Abstract

This study delves into the innovation of mini light-emitting diode (mini-LED) backlight module designs, a significant advancement in display technology. The module comprises a substrate, a receiving plane, and an LED structure, which uses a blue light with specific spectral characteristics. When combined with a red-green quantum dot (QD) film, it produces white light. For improved illumination uniformity, the Mini-LED structure was optimized with a focus on the thickness and concentration of layers, especially the TiO2 diffusion layer. A comprehensive design methodology using LightTools (8.6.0) optical simulation software was employed, linked with MATLAB (R2022a) for varied parameters and using the double deep Q-network (DDQN) algorithm via Python as a reinforcement learning agent. This approach facilitated optimal architecture design based on illumination uniformity. Also, the bidirectional scattering distribution function (BSDF) was employed to calculate the scattering properties of the backlight module’s surface, providing accurate simulation results. The DDQN algorithm enhanced the learning design, reducing simulation runs by 76.7% compared to traditional methods. The optimized solution achieved an impressive illumination uniformity of 83.8%, underscoring the benefits of integrating advanced algorithms into display technology optimization.

Keywords

• double deep Q-network
• mini LED backlight module
• quantum dot