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Source Mask Optimization1 (SMO) is one of the most important techniques available for extending ArF immersion
lithography. The combination of freeform source shape and complex mask pattern, determined by SMO, can extend the
practical resolution of a lithography system. However, imaging with a small k1 factor (~0.3 or smaller) is very sensitive
to many imaging parameters, such as illumination source shape error, lens aberration, process property, etc. As a result,
the real source shape must be re-adjusted to realize the expected imaging performance as may be seen, for example, in
an Optical Proximity Effect (OPE) curve.
In this paper we present an illumination pupilgram re-adjustment method that can effectively control the various
illumination parameters to get optimum imaging performance, which is required for the lithography process design.
The modulation functions are called Zernike intensity/distortion modulations2. Since the pupilgram modulation is
expressed by Zernike polynomials3, a high degree of pupilgram adjustment freedom is provided to the intelligent
illuminator4 (freeform illumination) which can be effectively modeled in the optimization. Furthermore, the
magnitude of each adjusting Zernike component can be restricted to prevent over modulation, which may affect imaging
performance for various patterns on a mask. Furthermore, the linear impact of each term of Zernike modulation can
allow us to use Zernike linear combination analysis to calculate imaging performance. Therefore, optimization using a
large variety of illumination modulation terms may be possible with reasonable computation loads.
The method is combined with an imaging simulator that includes resist models and optimization algorithms in
pupilgram refinement software called "OPE Master". The software can take into account the scanner signature and
various constraints so that the result of the optimization can be accurately realized on the scanner. The optimization can
be performed based on rigorous imaging simulation and Zernike linear combination analysis, which is based on a precalculated
Zernike linear sensitivity table.
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