Genetic-algorithm-based nonlinear calibration method for the digital micromirror device

As an important modulation device for optical imaging, precision detection, and high-end display, the nonlinear error of the digital micromirror device (DMD) has become a key performance-limiting factor. Traditional calibration methods rely on simplified models, struggle to characterize full-range nonlinearity, and cannot directly optimize the key parameter, like micromirror flipping time. To address these challenges, this paper proposes a genetic algorithm (GA)-based nonlinear calibration method. By using a photodetector (PD) for real-time light intensity feedback and GA to optimize the flipping time, high-precision calibration is achieved. Simulations and experiments demonstrate global optimization and uniform modulation, with DMD linearity improved by 42.2% post-calibration. Optimal parameters show strong adaptability across frame rates, enhancing optimization efficiency. Notably, the method remains effective under hardware-limited high frame rates, expanding the working range and applicability.