Background
The resist model is a significant element of computational lithography. Accurate calibration of the resist model is essential to obtain predictive results that closely match the exposure results on wafers. The trade-off between model accuracy and runtime is a challenging task. The selection of critical patterns used in model calibration affects these two metrics directly.
Aim
Having a dependable method for choosing critical patterns during resist model calibration is essential for lithography engineers with diverse professional backgrounds. Equally important is the ability to decrease runtime while maintaining the precision of calibrated resist models. To address these concerns, we present an approach for selecting critical patterns utilized in resist model calibration.
Approach
Since the spectrum carries pattern information, critical patterns are selected based on the coverage of the spectrum in the frequency domain. The spectrum coverage (SCO) of each pattern in the entire test pattern set is calculated according to the frequency and amplitude of all the spectra. Combinations of critical patterns are selected based on their SCO values. The optimal combinations include the most types of spectra and aim to adapt the calibrated model for broad applicability, encompassing both universal and unique patterns.
Results
The model verification results are compared with the experience-based methods, which select critical patterns based on the pitch-to-CD ratio. The accuracy and effectiveness of the proposed methodology have been demonstrated through experimental results. Compared with experience-based methods that only have dense and isolated critical patterns, our proposed method has a 9.8% increase in accuracy and a 35% decrease in runtime. Even compared with experience-based methods that include forbidden pitches, our method still achieves a 6.4% increase in accuracy without increasing runtime.
Conclusions
In summary, the suggested approach for selecting critical patterns based on SCO surpasses the experience-based methods in terms of both accuracy and efficiency. It can significantly shorten the modeling cycle of resists.
