Semiconductor processing is becoming more challenging as integrated circuit dimensions shrink. An increasing number of technologies are being developed for the purpose of ensuring pattern fidelity, and source and mask optimization (SMO) method has outstanding performances. In recent times, owing to the development of the process, more attention has been paid to the process window (PW). As a crucial parameter in lithography, the normalized image log slope (NILS) is strongly correlated with the PW. However, previous methods ignored the NILS in the inverse lithography model of the SMO. They regarded the NILS as the measurement index for forward lithography. This implies that the optimization of the NILS is the result of passive rather than active control, and the final optimization effect is unpredictable. In this study, the NILS is introduced in inverse lithography. The initial NILS is controlled by adding a penalty function to ensure that it continuously increases, thus increasing the exposure latitude and enhancing the PW. For the simulation, two masks typical of a 45-nm-node are selected. The results indicate that this method can effectively enhance the PW. With guaranteed pattern fidelity, the NILS of the two mask layouts increase by 16% and 9%, and the exposure latitudes increase by 21.5% and 21.7%.
With the continuous reduction of critical dimension (CD) of integrated circuits, inverse lithography technology (ILT) is widely adopted for the resolution enhancement to ensure the fidelity of photolithography, and for the process window (PW) improvement to enlarge the depth of focus (DOF) and exposure latitude (EL). In the photolithography, DOF is a critical specification which plays a vital role for the robustness of a lithographical process. DOF has been investigated to evaluate the optimization quality of ILT, but there is not a clear scenario to optimize the DOF directly. In this paper, the source and mask optimization (SMO) based on defocus generative and adversarial method (DGASMO) is proposed, which takes the source, mask and defocus as variables, and the inverse imaging framework employs the Adam algorithm to accelerate the optimization. In the optimization process, the penalty term constantly pushes the defocus outward, while the pattern fidelity pushes the defocus term inward, and the optimal source and mask are constantly searched in the confrontation process to realize the control of DOF. Compared to SMO with the Adam method (SMO-Adam), the PW and DOF (EL = 15%) in DGASMO maximally increased 29.12% and 44.09% at 85 nm technology node, and the PW and DOF (EL = 2%) at 55 nm technology node maximally increased 190.2% and 118.42%. Simulation results confirm the superiority of the proposed DGASMO approach in DOF improvement, process robustness, and process window.
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