Source optimisation (SO) is an approved approach to improve the imaging quality in inverse lithography techniques. It is critical to apply an optimisation approach with high convergence efficiency and minimum errors in pixel-based SO. To improve the convergence efficiency of the pixel-based SO, a route of particle swarm optimiser (PSO) combined with the adaptive nonlinear control strategy (ANCS) is proposed in this study. As a global optimisation algorithm, ANCS-PSO has the attributes of breaking away from the local optimum by adjusting the particle learning factor adaptively. In addition, the nonlinear control approach can broaden the search range and speed up the convergence of the iteration operation. The proposed approach also is compared with the linear decreasing inertia weight strategy and the simulated annealing strategy. The performance verification simulation displays the validity of PSO-ANCS and its potentials in SO with high convergence efficiency and optimisation capacity, by comparing the linear decreasing inertia weight strategy and the simulated annealing strategy.
Source mask optimization (SMO) is an effective method for improving the image quality of high-node lithography. Reasonable algorithm optimization is the critical issue in SMO. A GA-APSO hybrid algorithm, combining genetic algorithm (GA) and adaptive particle swarm optimization (APSO), was proposed to inversely obtain the global optimal distribution of the pixelated source and mask in the lithographic imaging process. The computational efficiency was improved by combining the GA and PSO algorithms. Additionally, the global search and local search were balanced through adaptive strategies, leading to a closer result to the global optimal solution. To verify the performance of GA-APSO, simple symmetric patterns and complex patterns were optimized and compared with GA and APSO, respectively. The results show that the pattern errors (PEs) of the resist image optimized by GA-APSO were reduced by 40.13–52.94% and 10.28–33.31% compared to GA and APSO, respectively. The time cost of GA-APSO was reduced by 75.91–87.00% and 48.43–58.66% compared to GA and APSO, respectively. Moreover, repeated calculation showed that the GA-APSO results were relatively stable. The results demonstrate the superior performance of GA-APSO in efficiency, accuracy, and repeatability for source and mask optimization.
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