Photolithographic Image Prediction with Conditional Adversarial network and Parameter Encoding

Photolithography is a pivotal stage in integrated circuit chip manufacturing, exerting a direct influence on both the performance and yield of the chips. Its efficacy hinges heavily on the meticulous control of parameters such as focus and exposure dose. Traditionally, the production speed is limited by multiply rounds of lengthy production-adjust process. Speeding up this process in manufacturing has become a pressing problem. To tackle this challenge, we introduce a novel framework that integrates a conditional adversarial network (GAN) with a parameter encoding module to predict the SEM images from layout images coupled with photolithography parameters. During the training phase, we first pre-train the model using paired data from layout images to SEM images, then we fine-tune the model with paired image data and corresponding lithography parameters. This proposed adversarial training process ensures that the generated photolithography images are remarkably similar to authentic SEM images. Moreover, the innovative parameter encoding structure allows the GAN to tailor image generation according to specific lithography parameters. Extensive experiments validate the effectiveness of our method, indicating that we have constructed a precise virtual photolithography model capable of predicting SEM images based on layout and parameter inputs. This approach not only effectively forecasts lithography outcomes but also provides essential technical support to address design challenges in the photolithography process, significantly streamlining the path from design to production.