Mask optimization for optical lithography based on UNet

Abstract Traditional photolithography methods are increasingly unable to meet the ever-stringent demands for pattern resolution and alignment accuracy, and developing efficient mask optimization techniques has become an urgent need in the industry. This paper delves into the application of Inverse Lithography Technology (ILT) in nanoscale photolithography and proposes an improved mask optimization algorithm. The backbone network of this algorithm employs a UNet architecture, trained initially on a prepared dataset comprising original masks and the ones optimized through traditional methods. The pre-trained backbone network generates a coarse mask, which is then inputted into a correction layer to refine the mask, enhancing pattern accuracy and processing efficiency. Compared to traditional gradient-based mask optimization methods, neural ILT demonstrates superior effectiveness, which enhances the efficiency and pattern quality of the lithography process while reducing production costs.