Focus enhancement for image-based overlay metrology targets via generative models

BackgroundImage-based overlay (IBO) target is a type of overlay metrology method in semiconductor manufacturing. Focus optimization is a critical factor that determines the quality of the IBO target signals in IBO target measurement. To calculate the overlay, two target images are required. As the semiconductor process becomes complex, optimizing the IBO target focus for both targets becomes increasingly difficult.AimWe target to develop a deep-learning-based method that tackles two issues in IBO target focus optimization. Instead of choosing a compromised focus offset for both targets or measuring different targets sequentially, we train a generative model that outputs IBO target images with different focus offsets. We use the model for the IBO target focus optimization.ApproachThe proposed method consists of two steps. The first step is to train a model; conditional deblurring network (cDN) generates IBO target images with different camera focus offsets. Our model is based on the deep image deblurring methods using the generative models with auxiliary focus offsets. The second step is to optimize IBO target images with the trained model. We can generate an optimal target image that consists of IBO target images with the best focus for each layer.ResultsIn experiments, we have compared different baseline models for cDN. The best model, which adopted DeblurGAN-v2, achieved a peak signal-to-noise-ratio of 49.04, a Fr’echet inception distance of 1.11, a mean squared error (MSE) of contrast precision of 0.0010, and an MSE of center-of-symmetry of 0.0672. The model can generate focus-adjusted IBO target images comparable to the ground truth. With the trained model, we optimized IBO target images. We achieved the improved target quality compared with the compromised best focus images.ConclusionsWe proposed a deep-learning-based methodology to solve the focus optimization problem for IBO target images. We demonstrated that our method can solve the IBO target optimization problem more efficiently than existing methods. In addition, we proved the applicability of deep learning in semiconductor manufacturing.