Trainable die-to-database for large field of view e-beam inspection

Abstract

Extreme ultraviolet lithography has advanced microfabrication of semiconductor devices toward the sub-10-nm generation. In this situation, stochastic defects increase and hence process evaluation requires an entire wafer inspection at high speed. To satisfy this requirement, a large field of view (FoV) inspection with low-resolution enables us to inspect an entire wafer within an acceptable time because the throughput of e-beam inspection depends on imaging resolution. However, low-resolution images are difficult to inspect at high precision using conventional methods because of a smaller photographed defect size and worse signal-to-noise ratio. Moreover, deformation caused by the manufacturing process and larger distortion caused by large FoV result in false detections when we apply die-to-database (D2DB) inspection. To solve these issues, we propose trainable D2DB inspection, which predicts a pixel-value distribution of normal images from a corresponding design layout. The proposed method is robust to low-resolution images because it considers noise and acceptable deformation as variance of the learned distribution. In addition, by introducing a model to predict a misalignment between a design layout and inspection image, trainable D2DB becomes robust to image distortion. Experiments show that trainable D2DB can perform high-precision inspection on images with large noise and image distortion.