Aberration analysis and compensate method of a BP neural network and sparrow search algorithm in deep ultraviolet lithography.

Abstract

Mass production can be planned by utilizing the multiple patterning technology of 193 nm immersion scanners at the 7 nm technology node. In deep ultraviolet lithography, imaging performance is significantly affected by distortions of projection optics. For 7 nm immersion lithography layer patterns, distortions of the projection optics must be tightly controlled. This paper proposes an optimization method to determine the distribution of Zernike aberration coefficients. First, we build aberration prediction models using the backpropagation (BP) neural network. Then, we propose an aberration optimization method based on the sparrow search algorithm (SSA), using the common indicators of the lithography process window, depth of focus, mask error enhancement factor, and image log slope as the objective function. Some sets of optimized aberration distributions are obtained using the SSA optimization method. Finally, we compare the results of the SSA optimization algorithm with those obtained by rigorous computational simulations. The aberration combination distribution optimized by the SSA method is much more significant than the value under the zero aberration (ideal conditions), a nonoptimal distribution in deep ultraviolet lithography image simulation. Furthermore, the results indicate that the aberration optimization method has a high prediction accuracy.