No abstract

In this work, correction techniques in the spatial and frequency domains are applied to improve the accuracy of less rigorous but more efficient mask models. This allows to reproduce the electromagnetic field (EMF) effects predicted by the rigorous model preserving the simplicity of the Kirchhoff model. In the frequency domain, two approaches are considered. First, a Jones pupil function is introduced in the projector pupil plane to describe amplitude, phase and polarization effects which are introduced by the mask. Second, a correction process performed directly on the scalar spectrum is used to tune the diffraction orders that get into the pupil of the optical projection system. Since a vector imaging description is needed to include the polarization phenomena, the spectra of the different polarization components are constructed from the scalar spectrum using correspondingly calibrated filters. In the spatial domain the well-known boundary layer model is considered.1 The bright features of the thin mask are surrounded with a semi-transparent region with a certain width, transmission and phase. Alternatively, the bright mask features of the Kirchhoff model are modified by adding delta functions to the edges of the absorber. All correction functions for spatial and frequency are obtained by a calibration with a rigorous model. The validity of these filtering techniques for different feature sizes and pitches is investigated

Different mask models have been compared: rigorous electromagnetic field (EMF) modeling, rigorous EMF modeling with decomposition techniques and the thin mask approach (Kirchhoff approach) to simulate optical diffraction from different mask patterns in projection systems for lithography. In addition, each rigorous model was tested for two different formulations for partially coherent imaging: The Hopkins assumption and rigorous simulation of mask diffraction orders for multiple illumination angles. The aim of this work is to closely approximate results of the rigorous EMF method by the thin mask model enhanced with pupil filtering techniques. The validity of this approach for different feature sizes, shapes and illumination conditions is investigated