Deep Learning for Chromatic Optimization in Dual‐Color Mini‐LEDs for Aircraft Displays

Abstract GaN‐based miniaturized light‐emitting diodes (mini‐LEDs) have emerged as highly promising light sources for high‐performance aircraft cockpit displays. Among these diodes, vertically stacked blue‒green dual‐color mini‐LEDs are fabricated and show excellent color‐tunable properties. When packaged with K 2 SiF 6 :Mn 4+ , an impressive color gamut area ratio of 113.63% NTSC is demonstrated. However, vertical optical crosstalk originates from the photoluminescence (PL) effect of the green epitaxial layer when the blue mini‐LED is powered on, making precise chromatic characteristics difficult to obtain. To address this problem, a deep neural network (DNN) is proposed, which combines forward modeling and inverse design in a tandem architecture. This DNN supports a current modulation scheme that enables precise control of chromaticity coordinates, achieving a Δ u ′ v ′ of 0.003. These advancements in materials and device strategies pave the way for developing low color difference, high‐resolution display systems for aircraft cockpits.