Enhanced multipopulation GA-BBO with chaotic acceleration for digital lithography mask optimization

SignificanceHigh-precision digital maskless lithography is essential for advanced integrated circuit fabrication, but existing mask-design methods struggle to simultaneously achieve superior pattern fidelity and computational efficiency, especially for complex nested structures.AimWe introduce a multistrategy collaborative optimization framework that integrates genetic algorithm, biogeography-based optimization, and tent map-based chaotic mutation to enhance both accuracy and speed in digital photomask design.ApproachBuilding on the Hopkins imaging model and a tailored multiobjective fitness function, the proposed method merges GA’s local search with BBO’s global migration strategy. To accelerate convergence and avoid premature stagnation, adaptive parameter tuning, chaotic mutation via a tent map, and an early stopping criterion are incorporated, ensuring a balance between exploration and exploitation without excessive computation.ResultsFor complex nested geometries, the proposed Tent–MP–GA–BBO framework achieved a PE of 1 pixel, maintained SSIM values above 0.99, and reduced computational time by up to 40% compared with the MP–GA–BBO baseline, demonstrating superior contour fidelity and optimization efficiency.ConclusionsThe integrated mapping strategy effectively harmonizes global exploration and local refinement, delivering a markedly more precise and efficient solution for high-precision maskless lithography mask optimization.