An efficient source optimization method using particle swarm optimization algorithm is proposed.The fidelity is adopted as the fitness function.Sources are encoded into particles,and then optimization is implemented by updating the velocities and positions of these particles.This method is demonstrated by using two typical mask patterns,including a periodic array of contact holes and a complex pattern with cross gate design.The pattern errors are reduced by 66.1% and 27.3%,respectively.The results show that the proposed method leads to faster convergence than the source optimization method using genetic algorithm while improving the image quality at the same time.The robustness of the proposed method is also verified by adding aberrations and defocus respectively.