DeePEB: A Neural Partial Differential Equation Solver for Post Exposure Baking Simulation in Lithography

Post Exposure Baking (PEB) has been widely utilized in advanced lithography. PEB simulation is critical in the lithography simulation flow, as it bridges the optical simulation result and the final developed profile in the photoresist. The process of PEB can be described by coupled partial differential equations (PDE) and corresponding boundary and initial conditions. Recent years have witnessed growing presence of machine learning algorithms in lithography simulation, while PEB simulation is often ignored or treated with compact models, considering the huge cost of solving PDEs exactly. In this work, based on the observation of the physical essence of PEB, we propose DeePEB: a neural PDE Solver for PEB simulation. This model is capable of predicting the PEB latent image with high accuracy and >100 × acceleration (compared to the commercial rigorous simulation tool), paving the way for efficient and accurate photoresist modeling in lithography simulation and layout optimization.