The Inverse Optimization of Lithographic Source and Mask via GA-APSO Hybrid Algorithm

Source mask optimization (SMO) is an effective method for improving the image quality of high-node lithography. Reasonable algorithm critical issue in SMO. A GA-APSO hybrid algorithm, combining genetic (GA) and adaptive particle swarm (APSO), was proposed to inversely obtain global optimal distribution pixelated source lithographic imaging process. The computational efficiency improved by GA PSO algorithms. Additionally, search local were balanced through strategies, leading a closer result solution. To verify performance GA-APSO, simple symmetric patterns complex optimized compared with APSO, respectively. results show that pattern errors (PEs) resist reduced 40.13–52.94% 10.28–33.31% time cost 75.91–87.00% 48.43–58.66% Moreover, repeated calculation showed relatively stable. demonstrate superior efficiency, accuracy, repeatability optimization.