Residual Neural Network Compensation of Temperature‐Induced Gamma Shift in OLED Display

Organic light‐emitting diode (OLED) displays exhibit temperature‐induced chromatic shifts and luminance‐dependent nonlinear responses, causing CIEDE2000 color difference (Δ E ) and local gamma ( γ ) to exceed production tolerances, particularly at low luminance. In high‐volume manufacturing, online calibration is typically limited to room temperature (28°C), as a 15‐point gamma sweep requires about 237.7 s per temperature; extending calibration to three temperatures raises the total time to approximately 713 s, making online multitemperature 3D‐LUT calibration impractical. Consequently, room‐temperature calibration with averaged offline gamma‐LUTs is commonly adopted, degrading low‐gray accuracy. This work proposes a compact Residual Neural Network (Residual NN) that predicts per‐gray RGB drive corrections using white‐only sensing on a sparse grid. The model incorporates normalized gray level, local gamma, and chromaticity offsets (Δ x , Δ y ) relative to D65. Evaluated across three luminance tiers and three temperatures, the proposed method improves Δ E compliance and gamma stability while reducing calibration time by 3.4× (713–207.6 s). Compared with a multitemperature 3D‐LUT baseline, Δ E compliance improves by up to 4.47%, and the gamma failure rate is reduced by 4.13% relative to γ = 2.2.