The precise metrology for edge placement error (EPE) is required especially in EUV era. Last year, we proposed new contour extraction algorithm using machine learning and verified the robustness to SEM noise on AEI pattern. In this study, we suggest the method for contour extraction on ADI pattern and improve the EPE measurement accuracy. It is known that the gray-level signal profile across the pattern edge on SEM image is varied depending on e-beam scan direction angle to the pattern edge, and especially the contrast of parallel pattern edge to scan direction is low and unstable. In addition, in case of ADI, the gray-level of SEM image are varied and have the shading because of charge effect caused by e-beam exposure on the pattern. Therefore, the contour extraction on ADI pattern just using simple feature value or some of thresholds is usually inaccurate. However, the precise contour extraction independent on e-beam scan direction is required strongly for 2D pattern inspection and metrology. In this paper, we will propose the novel contour extraction method of precise EPE metrology on ADI regardless of the ebeam scanning direction to the pattern edge. We use machine learning to extract contour, splitted training data according to target edge direction, and trained contour extraction model. This model is expected to learn not only the gray-level variation but also the drift of landing position caused by the charge effect on ADI. We captured SEM images on the ADI wafer with several scan direction and compared between the contours extracted by the conventional method and extracted by the proposal method, then the improvement of EPE measurement accuracy at every pattern direction on ADI is verified.
The accurate and precise contour extraction on SEM image is important to measure overlay, improve OPC model, inspect tiny hot-spot, and so on. In 2019, we reported about the measurement repeatability of edge placement error (EPE) with Die-to-Database (D2DB) algorithm. In this study, we apply machine learning for the contour extraction to improve the measurement throughput with high accuracy and precision of EPE on 2D pattern. The pattern contour on SEM image can be extracted by processing gray-level profile data across the measurement line. Generally, in order to extract the precise contour, the direction of the profile should be perpendicular to the pattern contour. Although the direction used to be determined by the design pattern, it can’t be accurate enough to extract the contour exactly since the shape between the design pattern and the actual pattern are different. We propose the method that determines the direction of the profile acquisition using the contour taken by machine learning, which is more similar to the actual pattern contour than the design pattern contour. The accuracy and the precision of EPE measurement using the contour extracted by our method has been improved in actual SEM images captured repeatedly.