Fast and accurate machine learning assisted mask optimization

Abstract

Accurate prediction of Jacobian is essential for multi-variable optical proximity correction (OPC). The Jacobian means the small variation of edge placement error (EPE) induced by small mask bias of nearby segments under optical proximity effects. If the Jacobian can be accurately calculated, it is helpful for OPC iteration reduction, or EPE improvement for 2D shape mask patterns. Moreover, if this can be cost-effective, this approach can be easily extended to Full chip level. We changed expensive Jacobian matrix procedure into simple ML based Jacobian model inference. Thanks to efficiently chosen geometric and optical features and light ANN structure, our method can predict Jacobian 76% faster and 81% more accurate than intensity distribution function method. We also improved mask optimization algorithm by inserting small gradient iterations. Our mask optimization solver was 2 times faster than vanilla mask optimization solver. Through this effort, we constructed fast and accurate machine learning assisted mask optimization solver.