Background: In-line metrology for three-dimensional (3D) profiling high-aspect-ratio (HAR) features is highly important for manufacturing semiconductor devices, particularly for memory devices, such as 3D NAND and DRAM.
Aim: Our purpose was to obtain the cross-sectional profiles of the HAR features from top-view critical dimension scanning electron microscopy (CD-SEM) images.
Approach: Based on Monte Carlo simulation results, we proposed a method for 3D profiling of HAR features using backscattered electron (BSE) signal intensities. Several kinds of HAR holes with different taper angles and bowing geometries were fabricated. High-voltage CD-SEM was used for experiments to determine the feasibility of our approach.
Results: Using the BSE line-profile, we constructed cross sections of the taper holes and estimated sidewall angles (SWAs), which were approximately the same as those observed using field-emission scanning electron microscopy (FE-SEM). The constructed cross sections of the bowing holes and the trends of the geometric variance, which were estimated by the middle CD and its depth, were consistent with the cross sections observed by FE-SEM.
Conclusions: The results demonstrate that the variation in the HAR holes, such as SWA and bowing geometry, can be measured and monitored using the BSE images.
We applied deep learning techniques to improve the accuracy of 3D-profiling for high aspect ratio (HAR) holes. As deep learning requires big data for training, we developed a method for generating a large amount of BSE line-profiles by a numerical calculation in which the aperture angle and the aberration effects of the electron beam are considered. We then utilized these numerically calculated datasets to train the deep learning model to learn the mapping from the BSE line-profiles to the target cross-sectional profiles of the HAR holes. Two different one-dimensional neural network architectures: convolutional neural network (CNN) and multi-scale convolutional neural network (MS-CNN) were trained, and different loss functions were investigated to optimize the networks. The test results show that the MS-CNN model with a defined loss function of weighted mean square error (WMSE) provided higher accuracy than the others. The mean absolute percentage error (MAPE) distribution was narrow and the typical MAPE was 4% over 2810 items of test data. This model enables us to predict the cross-section of the HAR holes with different sidewall profiles more accurately than our previously proposed exponential model. These results demonstrate the effectiveness of the learning approach for improving the accuracy of 3D-profiling of the HAR features.
In-line metrology for measuring 3D features of the high aspect ratio (HAR) holes is becoming more challenging due to the development of semiconductor technology, particularly in memory devices. Measurements of the bottom critical dimension (CD), taper angles, and 3D profiles of the HAR holes require new imaging capabilities.
In this work, we explore backscattered electron (BSE) imaging and its applicability in 3D metrology of the HAR holes. Monte Carlo simulations were performed to estimate the BSE signals emitted from the HAR holes. The simulation results demonstrate that the BSE signal intensity decreases exponentially with increasing the depth of the irradiated location in the HAR holes. Based on the characteristics of the BSE signal intensity, an algorithm utilizing depth-correlated BSE signal intensity was proposed for the 3D metrology of the HAR holes. Furthermore, several types of holes with different taper angles and different bowing profiles were fabricated and experiments were performed to verify the feasibility of the proposed algorithm. The cross-sectional profiles of the fabricated holes which are created using the BSE profiles are matching with the as-cleaved cross section observed by X-SEM. These results demonstrate that the 3D-profile variation of the HAR holes induced by the etching processes can be identified by our approach.