A Fast and Flexible Library-based Thick Mask Near-field Calculation Method

Aerial image calculation is the basis of the current lithography simulation. As the critical dimension (CD) of the integrated circuits continuously shrinks, the thick mask near-field calculation has increasing influence on the accuracy and efficiency of the entire aerial image calculation process. This paper develops a flexible library-based approach to significantly improve the efficiency of the thick mask near-field calculation compared to the rigorous modeling method, while leading to much higher accuracy than the Kirchhoff approximation method. Specifically, a set of typical features on the fullchip are selected to serve as the training data, whose near-fields are pre-calculated and saved in the library. Given an arbitrary test mask, we first decompose it into convex corners, concave corners and edges, afterwards match each patch to the training layouts based on nonparametric kernel regression. Subsequently, we use the matched near-fields in the library to replace the mask patches, and rapidly synthesize the near-field for the entire test mask. Finally, a data-fitting method is proposed to improve the accuracy of the synthesized near-field based on least square estimate (LSE). We use a pair of two-dimensional mask patterns to test our method. Simulations show that the proposed method can significantly speed up the current FDTD method, and effectively improve the accuracy of the Kirchhoff approximation method.