Mask Optimization

Mask optimization focuses on designing optimal masks—patterns used to transfer designs onto materials—to improve various processes, from semiconductor chip manufacturing to image inpainting. Current research emphasizes the use of machine learning, particularly deep neural networks (including GANs and convolutional neural operators), and reinforcement learning algorithms to accelerate and improve the quality of mask generation, often integrating physics-based models for enhanced accuracy. These advancements offer significant potential for increased efficiency and improved results in diverse fields, ranging from faster and more precise chip fabrication to more effective image restoration techniques. The development of faster and more accurate mask optimization methods is driving progress across multiple scientific and engineering disciplines.