Si/SiGe heterostructures are gaining traction as a starting template in applications such as Gate-All-Around Field-Effect Transistor (GAAFET), complementary FET (CFET), and 3-dimensional dynamic random access memory (3D-DRAM), where the SiGe alloy plays the role of sacrificial material for channel release. However, the formation of crystalline defects (e.g. crosshatch) in the epitaxially grown layers plays a critical part in determining the overall device performance. As such, it is key to be able to control the defectivity level using large surface area inspection techniques. The challenge of such inspection is that it must combine a high enough throughput to detect low-density defects together with sensitivity to nanometer size defects. In addition, the technique should also be able to distinguish these elongated one-dimensional crystalline defects from other types of defects. In this study, we investigate the impact of the number of Si/SiGe bilayers on the crystal defect distribution utilizing a combined approach of optical inspection and extensive e-beam review for both qualitative and quantitative defect characterization. In-line optical inspection techniques revealed that the crosshatch density and distribution varied significantly with the number of Si/SiGe bilayers. These observations were then confirmed by high-resolution e-beam review coupled with image analysis and signal processing to enable crosshatch quantification. Our approach considers an initial investigation on thin Si/SiGe bilayers (up to ~5x bilayers) and is further extended to thick stacks (up to 60x bilayers) to evaluate the capability of optical inspection as the high-throughput reference technique. In conclusion, this study aims to develop a methodology to investigate the crosshatch density in Si/SiGe superlattices, using optical inspection and e-beam review as main characterization tools. These techniques offer valuable insights in terms of defect distribution at the wafer level for the design and fabrication of next-generation semiconductor devices.
Extreme Ultraviolet lithography with a numerical aperture of 0.55 will bring an improved optical contrast for contact hole layers and via layers. This improved optical contrast should lead to a reduction in the number of stochastic defects for these layers. To quantify this reduction, an adequate inspection methodology is required that can detect, in addition to the standard missing and merging defects, contact holes that are only partially opened. In this work we demonstrate a technique that uses backscattered electrons to detect these defects. In the first phase the beam-settings in a top-down scanning electron microscope are optimized to visualize holes that have been confirmed to be partially opened contact holes by either voltage contrast or transmission electron microscope. In the second phase these beam conditions are implemented on a massive metrology e-beam tool that has an increased throughput and therefore can collect information on millions of contact holes. In the last phase we show how this inspection can be used to enlarge the failure free latitude on a 36nm hexagonal contact hole pattern and to optimize the litho and etch conditions to minimize the number of stochastic defects on product wafers.
The transistor architecture of complementary FET (CFET) is attractive for scaling down in technology nodes beyond 1 nm. CFET comprising vertically stacked nMOS and pMOS can be integrated monolithically and sequentially. The monolithic process is cost effective but complex because it requires patterning of high-aspect-ratio (HAR) structures and vertical edge placement control for stacked n-p nanosheet channels. It also brings challenges to in-line metrology in measuring the vertical dimension. In this work, we demonstrate a non-destructive, in-line metrology solution to measure the etch-back depth by CD-SEM. As the backscattered electron (BSE) signal intensity at the bottom of an HAR structure is determined by the structure's depth and top dimension, the depth can be monitored via an index based on the grey level and top dimension in CD-SEM images. Wafers with different etch-back depths were measured for evaluation of the M0 etch-back process in CFET integration. Good agreement was obtained between the etch-back depths measured by CDSEM and TEM. The flexible capability of CD-SEM to measure the depth and variation from extremely small areas to the wafer level could be helpful for CFET process control.
3D-NAND memory will continue to increase in the aspect ratio of channel holes. High throughput and in-line monitoring solutions for 3D profiling of high aspect ratio (HAR) features are the key for yield improvement. A deep learning (DL) model has been developed to improve the 3D profiling accuracy of the HAR features. In this work, the HAR holes with different bowing geometries were fabricated and a high-voltage CD-SEM was used to evaluate the performance of the DL model. The accuracy and the sensitivity of the DL model was evaluated by comparing the predicted cross-sections with the X-SEM measurement. The results show that the DL model enables the maximum CD (MCD) of the bowing features to be predicted with a sensitivity of 0.93 and its depth position to be predicted with a sensitivity of 0.91. The DL learning model reduced the absolute error of the predicted MCD depth position from several hundreds of nanometers, the error occurring when using the exponential model, to within 100 nm.
KEYWORDS: 3D metrology, 3D applications, Monte Carlo methods, Cadmium, Solids, Scanning electron microscopy, Critical dimension metrology, Signal detection, Semiconducting wafers, Metrology
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.
KEYWORDS: Monte Carlo methods, 3D metrology, Critical dimension metrology, Metrology, Scanning electron microscopy, Etching, Scattering, Electron beams
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.
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