Model-driven rule-based mask process correction

MPC has been a technology enabler since 32nm technology node, and the number of mask layers receiving MPC increases as technology node advances. Model-based Mask Process Correction (MB-MPC) has evolved from correction based on short-range Gaussian to full Machine Learning (ML) based model and correction. Model-based MPC has demonstrated efficacy in reducing mask error on advanced nodes, but often requires extensive computing resource to achieve the stringent mask fidelity and Critical Dimension (CD) requirements. On the other hand, rule-based Mask Process Correction (RB-MPC) has the advantage of fast turn-around time. This paper presents an approach to rule-based MPC that seeks to extract the maximum benefits of model-based MPC. The rules cover critical geometrical ‘building blocks’ such as lines, contacts, line-ends, notches. Derivation of the rules is guided by a mask process model. The goal of RB-MPC is to mitigate the long runtime of MB-MPC while minimizing loss in patterning fidelity. We will describe the methodology of rule derivation, implementation, and verification of RB-MPC. The RB-MPC approach meets accuracy requirements for 32-22nm technology nodes. For more advanced technology nodes, a hybrid RB-MB-MPC recipe is proposed to achieve both high accuracy and fast runtime.