Luqiao Yin

Abstract Reducing pixel crosstalk and enhancing luminance are crucial for improving image quality in micro-LED display applications. Although the Lambertian emission of micro-LEDs can be partially collimated by surface-integrated microlens arrays (MLAs), the collimation performance is often limited by the insufficient refractive index of conventional lens materials. In this work, we propose a direct on-chip integration of microlenses onto fabricated micro-LEDs in a pixel-to-pixel manner.

Abstract The rapid evolution of intelligent display technologies is driving the development of next-generation edge smart systems, yet conventional off-pixel processing architectures suffer from severe display latency and energy bottlenecks. Embedding in-memory computing features into pixel-level active drivers presents a promising strategy for co-locating image processing and display.

Surface relief grating waveguides are widely used in augmented reality (AR) devices such as AR glasses. However, the rapid design and optimization of such waveguides remain challenging, particularly in large-scale devices such as augmented reality head-up displays. We propose a method for optimizing the brightness uniformity of waveguide exit pupils using deep neural networks. The accuracy of the model is further enhanced through Bayesian optimization and particle swarm optimization.