Optical lithography is one of the key technologies in semiconductor material and device fabrications. It is the process to transfer the layouts of desired pattern onto the wafers. However, the exposure on wafer has distortions due to the proximity effects. As the minimum feature sizes of explored samples continue to shrink, the mismatch between the pattern and the actual result on wafer is significant. Correction of mask patterns between the sample and post exposure result is thus necessary. Optical proximity correction (OPC) is the process of modifying the polygons to compensate for the non-ideal properties of the lithography process. Given the shapes desired on the wafer, the mask is modified to improve the reproduction of the critical geometry. In this work, we propose an intelligent OPC technique for process distortion compensation of layout mask. The proposed system consists of three parts: the pre-process, the OPC engine, and the post-process. During the pre-process, the partition analyzer will analysis all patterns and then divided them into many segments. Secondly, the OPC module is applied to correct the mask. The intelligent module searches the whole problem domain to find out the best combination of the mask shape by the genetic algorithm (GA). The corrected mask is verified by performing lithographic simulation to get the error norm between exposed result and desired layout. Finally, the mask verification is conducted in the post-process. To perform the mask correction in sub-wavelength era, the developed intelligent OPC technique integrates the GA and the model-based technique. We also propose a specific parameter extraction strategy for the OPC problem. Accuracy and computational efficiency of the methods are justified by a series testing and comparison between fundamental patterns and experiment data. We believe it is a constructive approach to advanced computer aided design for fabrication of semiconductor material, device, and integrated circuit.

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