Aberration optimization in an extreme ultraviolet lithography projector via a BP neural network and simulated annealing algorithm.

Abstract

Extreme ultraviolet lithography (EUVL) presents promise for the advanced technology node in the manufacturing of integrated circuits. The imaging performance of EUVL is significantly affected by the aberration of projection optics. To obtain one optimum aberration for different test patterns, an inverse optimization method for aberration is proposed in this paper. The aberration models of three types of test patterns are first established by applying the backpropagation (BP) neural network. Then choosing the common indicators of the lithography process variation band (PVB) and pattern shift (PS) as the objective function, an aberration optimization method based on the algorithm of simulated annealing is proposed. After applying the optimization method, a set of optimized aberrations and the corresponding PVBs and PSs are obtained and analyzed. These results are finally compared with those from rigorous simulations. The comparison results show that zero aberration is non-optimal distribution in EUVL image simulation with mask topography. In addition, the high prediction accuracy and robustness of aberration optimization is also demonstrated from the results.