Efficient Mask Optimization for DMD-Based Maskless Lithography Using a Genetic–Hippo Hybrid Algorithm

Abstract

Mask optimization is a critical technique for enhancing imaging performance in digital micromirror device (DMD)-based maskless lithography. Conventional algorithms, however, often suffer from slow convergence and limited adaptability, particularly when handling complex multi-feature mask patterns. To address these challenges, this study proposes a hybrid Genetic–Hippo Optimization (GA-HO) algorithm that integrates the global exploration capability of the Genetic Algorithm (GA) with the local exploitation efficiency of the Hippocampus Optimization (HO) Algorithm. The approach employs grayscale modulation for adaptive mask optimization and introduces a global–local cyclic search mechanism to balance exploration and exploitation throughout the optimization process. Simulation results demonstrate that the GA-HO hybrid algorithm achieves a more pronounced improvement in overall optimization performance compared with the standard GA. In complex multi-line mask optimization, the standard GA achieves approximately a 18% enhancement in optimization accuracy, whereas the GA-HO algorithm achieves around a 30% improvement. Moreover, the GA-HO algorithm exhibits a smoother convergence curve, greater stability, and superior robustness. The hybrid method effectively suppresses linewidth variations and corner distortions caused by optical proximity effects (OPE), maintaining high imaging fidelity and stable optimization outcomes even under challenging mask conditions. Overall, the proposed GA-HO framework demonstrates excellent efficiency, adaptability, and precision, providing a reliable and high-performance solution for DMD-based maskless lithography. This work offers a strong theoretical and algorithmic foundation for advancing high-resolution, high-efficiency, and low-cost micro/nanofabrication technologies, highlighting the potential of heuristic hybrid optimization strategies for practical lithography applications.