Enhanced SEM Image Denoising via U-Net Based GAN Using Multi-Die Single Frame Images

With the advancement of integrated circuit technology nodes, distortion occurs in pattern transfer inevitably caused by Optical Proximity Effect (OPE). Model-based OPC (MB-OPC) is the most commonly used solution in the industry. MB-OPC relies on an accurate lithography model to guide it. However, numerous challenges are in face when tuning a lithography model. Scanning Electronic Microscope (SEM) images—the most critical data for lithography model tend to be noisy. The current method of image denoising is frame averaging, but it’s time-consuming and affected by resist shrinkage, leading to inaccuracy model. Deep learning methods in previous study lack real and accurate SEM images. We collected reliable SEM images from a fab by overlying Multi die single frame images, and a UNet-based GAN model is designed for denoising. Experiment demonstrates that MSE, PSNR, and SSIM of our methods are at least 9.1%, 2.7% and 1.4% better than other methods. Moreover, our method outperforms other methods in preserving process metrics like line widths and contour accuracy, closely approximating the target images.