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Background: Algorithmic breakthroughs in machine learning (ML) have allowed increasingly more applications developed for computational lithography, gradually shifting focus from hotspot detection to inverse lithography and optical proximity correction (OPC). We proposed a pixelated mask synthesis method utilizing deep-learning techniques, to generate afterdevelopment-inspection (ADI) contour and mask feature generation.Aim: Conventional OPC correction consists of two parts, the simulation model which predicts the expected contour signal, and the correction script that modifies the actual layout. With practicality in mind, we collected modeling wafer data from scratch, then implemented ML models to reproduce conventional OPC actions, mask to contour prediction, and design to mask correction.Approach: Two generative adversarial networks (GANs) were constructed, a pix2pix model was first trained to learn the correspondences between mask image and paired ADI contour image collected on wafer. The second model is embedded into machine learning mask correction (ML-OPC) framework, output mask is optimized through minimizing pixel difference between design target and simulated contour.Results: Two different magnification SEM image datasets were collected and studied, with the higher magnification showing better simulator pixel accuracy. Supervised training of the correction model provided a quick prototype mask synthesis generator, and combination of unsupervised training allowed mask pattern synthetization from any given design layout. Conclusions:The experimental results demonstrated that our ML-OPC framework was able to mimic conventional OPC model in producing exquisite mask patterns and contours. This ML-OPC framework could be implemented across full chip layout.

Suboptimal layout geometries after optical proximity correction (OPC) might induce lithography hotspot, and result in degradation of wafer yield during integrated circuit (IC) manufacturing. Conventional hotspot correction methods have been widely conducted on post-OPC layout, such as rule-based or model-based hotspot fixing, but these methods might not completely solve hotspot issues due to the time-consuming process or model inaccuracy. Over the past of few years, the explosive growth of machine learning techniques has boosted the capability of computational lithography including hotspot detection and correction. In this paper, we focus on lithography hotspot correction with Generative Adversarial Network (GAN) to modify pattern shapes of hotspot and further improve lithographic printing of designed layout. The proposed approach first built a hotspot correction model based on different types of lithography rule check (LRC) hotspots, by training a pix2pix model to learn the correspondences between paired post-OPC layout image and after development inspection (ADI) contour image simulated from LRC tool. Then, we input hotspot-free contour image created from original hotspot into the deep learning model to generate supposedly hotspot-free mask image, and converted the mask image back into polygonal layout. Finally, mask layout with hotspot were partially replaced with predicted mask layout, and then examined with LRC simulation. Furthermore, we also implemented transfer learning for new hotspots captured from new design layout to expand the capability of our hotspot correction flow. Experimental results showed that this methodology successfully corrected lithography hotspots and significantly enhanced the efficiency of hotspot correction.