A Novel Optical Proximity Correction (OPC) System Based on Deep Learning Method for the Extreme Ultraviolet (EUV) Lithography

Abstract

As one of the most important technologies for the next generation very-large scale integrated circuit fabrication, extreme ultraviolet (EUV) lithography has attracted more and more attention in recent years. However, in EUV lithography, the optical distortion of the printed image on wafer always has negative impacts on the imaging performance. Thus, to enhance the imaging performance of EUV system, especially for small critical dimensions, in this work, a novel optical proximity correction (OPC) system based on the deep learning technique is proposed. It includes a forward module and an inverse module, where the forward module is employed to fast and accurately map the mask to the corresponding near field of the plane above the stack to help the construction of training dataset for the inverse module operation, and the inverse module is employed to fast and accurately map the target printed image to the corrected mask. Numerical examples demonstrate that compared with traditional full-wave simulation, the forward module can greatly improve the computational efficiency including the required runtime and memory. Meanwhile, different from time consuming iterative OPC methods, the corrected mask can be immediately obtained as the target printed image is input using the trained inverse module.