TY - JOUR
T1 - Intelligent optical proximity correction using genetic algorithm with model- and rule-based approaches
AU - Li, Yi-Ming
AU - Yu, Shao Ming
AU - Li, Yih Lang
PY - 2009/3
Y1 - 2009/3
N2 - Optical lithography is one of the key technologies in semiconductor material and device fabrications. It is a 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 experimental result on wafer is significant. Corrections of mask patterns between the sample and post exposure result are thus necessary. Optical proximity correction (OPC) is the process of modifying the geometries of the layouts 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. To perform the mask correction in sub-wavelength era, two different strategies including the genetic algorithm (GA) with model-based OPC and the GA with rule-based OPC methods are examined. The proposed intelligent system consists of three parts: the pre-process, the OPC engine, and the post-process. During the pre-process, the pattern analyzer will analysis all patterns and then divided them into many segments for model-based OPC or generates assistant patterns for rule-based OPC. 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 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. By testing on several fundamental patterns, this approach shows good correction accuracy and efficiency, compared with experimentally fabricated samples. It can be applied to perform the mask correction in sub-wavelength era.
AB - Optical lithography is one of the key technologies in semiconductor material and device fabrications. It is a 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 experimental result on wafer is significant. Corrections of mask patterns between the sample and post exposure result are thus necessary. Optical proximity correction (OPC) is the process of modifying the geometries of the layouts 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. To perform the mask correction in sub-wavelength era, two different strategies including the genetic algorithm (GA) with model-based OPC and the GA with rule-based OPC methods are examined. The proposed intelligent system consists of three parts: the pre-process, the OPC engine, and the post-process. During the pre-process, the pattern analyzer will analysis all patterns and then divided them into many segments for model-based OPC or generates assistant patterns for rule-based OPC. 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 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. By testing on several fundamental patterns, this approach shows good correction accuracy and efficiency, compared with experimentally fabricated samples. It can be applied to perform the mask correction in sub-wavelength era.
KW - Genetic algorithm
KW - Lithography
KW - Model base
KW - Numerical simulation
KW - Optical proximity correction
KW - Rule base
UR - http://www.scopus.com/inward/record.url?scp=59749091761&partnerID=8YFLogxK
U2 - 10.1016/j.commatsci.2008.04.031
DO - 10.1016/j.commatsci.2008.04.031
M3 - Article
AN - SCOPUS:59749091761
SN - 0927-0256
VL - 45
SP - 65
EP - 76
JO - Computational Materials Science
JF - Computational Materials Science
IS - 1
ER -