Detailed Successive Layer Modeling and Design Factor Analysis for Single Micro-LED Pixel

The major factors associated with the manufactured structures and photon emission intensity distribution must be accurately identified for the complicated optical design parameters associated with micro-LED pixels. This study presents the methodologies and reveals the sequential calculating protocol in each layer for pixel-level optical simulations, based on which the boundary condition errors associated with common optical simulations can be corrected. Moreover, the optical effect on each epitaxial layer of the micro-LED design is revealed, and the rationality of the setting of the finite-radius receiver with a pseudo-extended substrate material is also explained with respect to the intrinsic and extrinsic pixel-intensity distribution properties. Finally, the design of experiments DOE L-18 orthogonal array table is demonstrated by the Taguchi method to explore the principal factors for the parameter design of the micro-LED layers. The results indicate that the sapphire thickness, the P-GaN layer, the buffer layers, and an additional substrate plate are the four significant factors that primarily influence the luminous power and the corresponding uniformity on the exit pupil of micro-LEDs. Based on the luminous power uniformity, the signal-to-noise ratio in the sapphire thickness is 16.9, and the extended substrate plate is 9.8, which is similar to 10 times > the interaction design parameter, such as the etching angle and reflective index of the extended substrate of the micro-LED pixel. These specific design factors can be the major optimization parameters for cost control and performance improvement.

Automated summary

This study accurately identifies the major factors associated with manufactured structures and photon emission intensity distribution, and presents a methodology for pixel-level optical simulations. The study reveals the optical effects of each layer in micro-LED design and explores the principal factors for parameter design using a design of experiments method. The specific design factors identified can be used for cost control and performance improvement.