ILT approach based on Convex Relaxation of Discrete Programming
Avideh Zakhor, Ma Xu1 and Shangliang Jiang
KLA Tencor and Signetron
Photolithography is a method of structuring material, used by the semiconductor manufacturing industry. Due to the limits of optical lithography, the electronics industry has relied on resolution enhancement techniques (RET) to compensate mask distortions as they are projected onto semiconductor wafers. One of the main techniques is model based-optical proximity correction (MB-OPC), which modifies the mask by altering the main feature and adding sub-resolution features (SRAFs) to the mask pattern. However, the recent shrinking technology node has caused many issues. First, the process results in complicated masks that are difficult to manufacture. Second, the run-time is long. Finally, MB-OPC is local. It does not consider the placement of SRAFs and instead requires them to be placed manually from rules. This project concentrates on the research of a fast Inverse Lithography Technology (ILT) algorithm. The proposed ILT method is tailored to address manufacturability and optimality. First, the ILT problem is relaxed into a continuous convex optimization problem. Within this framework convex constraints are enforced that capture the mask manufacturability. This algorithm is used within an integrated approach that solves a binary optimization problem. The proposed algorithm is expected to provide a method that considers global optimality while maintaining mask manufacturability.