Analysis and modulation of aberration in an extreme ultraviolet lithography projector via rigorous simulation and a back propagation neural network.

Lens aberration is a critical factor affecting lithography, one that deteriorates the image fidelity and contrast. As the perfect lens does not exist, the aberration control is important for real optical systems, especially for extreme ultraviolet lithography (EUVL). By choosing the process variation band (PVB) and pattern shift (PS) as the lithographic performance indicators, the inverse analysis model for aberration control is proposed in this paper. First, the effects of aberration with 36 Zernike terms on lithography performance are forward analyzed. Using the definitive screening design (DSD) and with the help of statistical analysis methods of analysis of variance and F test, the combined Zernike terms leading to prominent PVB and PS are identified. After giving a brief introduction of backpropagation neural network (BPNN), the aberration control model based on DSD and BPNN is then established. Finally, several examples are analyzed to demonstrate the effectiveness and robustness of the aberration control model. Predicted results show that the optimum distribution of Zernike coefficients given by the aberration model can generate minimum impact on imaging quality, and this impact is very close to that of zero aberration. The results demonstrate that the BPNN-based aberration model has the potential to be an efficient guiding method for controlling the aberration of EUVL in the optical design stage.