Generation of inverse assist features using generative adversarial networks

Abstract

Adding the assist feature in the optical proximity correction (OPC) procedure is crucial for improving the process window. The rule-based assist feature placement is very fast; however, the resultant process window still requires improvement. The model-based assist feature placement presents a better process window, but its iterative nature makes it extremely time-consuming. The assist features (AFs) that are optimized by using the inverse lithography technique perform best in terms of lithographic metrics such as the Depth of Focus, Mask Error Enhancement Factor, and Normalized Image Log Slope; these features can be added to the objective function. However, the inverse lithography technique is a pixel-level correction method, which is even more time-consuming. Consequently, it is typically used to provide guidance for rule-based AF placement. In this study, we design novel generative adversarial networks (GANs) that can generate AFs to increase the speed of assist feature placement while maintaining relatively high lithographic performance using inverse lithography techniques. The results demonstrate that the proposed generative adversarial network methods are three orders of magnitude faster than the inverse lithography techniques. Additionally, they exhibit lower edge placement errors and process variation bands when compared to the inverse lithography techniques.