Lithography Simulation

Lithography simulation aims to accurately predict the final shape of integrated circuits (ICs) after the manufacturing process, crucial for ensuring design manufacturability and performance. Current research focuses on developing faster and more accurate simulation methods, often employing deep neural networks—including architectures like generative adversarial networks (GANs) and convolutional neural networks (CNNs)—to replace computationally expensive traditional models. These advancements leverage techniques such as active learning and data augmentation to improve model accuracy and efficiency, ultimately reducing design-to-manufacturing discrepancies and accelerating the development of advanced semiconductor technologies.