P‐65: AI‐Driven Timing Optimization for Enhanced Visual Performance in HOP 3.0

Hybrid Oxide Polysilicon (HOP) 3.0 technology revolutionizes display performance by enabling ultra‐fast response times and exceptional visual fidelity. However, realizing the full potential of HOP 3.0 displays hinges on precise optimization of numerous timing parameters. Traditionally, this tuning process has been a time‐consuming and labor‐intensive manual endeavor, requiring expert knowledge and iterative adjustments. This paper introduces a machine learning driven solution to automate the timing optimization process for HOP 3.0 displays, thereby significantly accelerating development cycles and ensuring consistent high‐quality visual output. Our approach leverages models trained on a comprehensive dataset encompassing diverse timing parameter configurations and their corresponding Flicker (FLK) and Voltage Refresh Rate (VRR) characteristics. This model learns the complex relationships between timing parameters, display patterns, and luminance levels, enabling it to predict optimal timings with high accuracy. We rigorously evaluate our AI‐based optimization method across a wide range of display scenarios, showcasing its ability to consistently achieve superior Flicker and delta JND performance compared to manual tuning techniques. Furthermore, we analyze the computational efficiency of our approach, demonstrating substantial reductions in tuning time while maintaining exceptional visual quality. Our findings pave the way for streamlined HOP 3.0 display manufacturing, empowering engineers to rapidly iterate on designs and deliver cutting‐edge visual experiences with unprecedented efficiency.