KEYWORDS: Scanning electron microscopy, Education and training, Semiconducting wafers, Electron microscopes, 3D scanning, Data modeling, Critical dimension metrology, Scatterometry, 3D metrology, 3D modeling
Accurate metrology techniques for semiconductor devices are indispensable for controlling the manufacturing process. For instance, the dimensions of a transistor’s current channel (fin) are an important indicator of the device’s performance regarding switching voltages and parasitic capacities. We expand upon traditional 2D analysis by utilizing computer vision techniques for full-surface reconstruction. We propose a data-driven approach that predicts the dimensions, height and width (CD) values, of fin-like structures. During operation, the method solely requires experimental images from a scanning electron microscope of the patterns concerned. We introduce an unsupervised domain adaptation step to overcome the domain gap between experimental and simulated data. Our model is further fine-tuned with a height measurement from a second scatterometry sensor and optimized through a tailored training scheme for optimal performance. The proposed method results in accurate depth predictions, namely 100% accurate interwafer classification with an root-mean-squared error of 0.67 nm. The R2 of the intrawafer performance on height is between 0.59 and 0.70. Qualitative results also indicate that detailed surface features, such as corners, are accurately predicted. Our study shows that accurate z-metrology techniques can be viable for high-volume manufacturing.
KEYWORDS: Scanning electron microscopy, Line scan image sensors, Education and training, Simulations, Analytic models, Monte Carlo methods, Electron beams, Neural networks, Image analysis, Silicon
There is a growing need for accurate depth measurements of on-chip structures. Since Scanning Electron Microscopes (SEMs) are already regularly being used for fast and local 2D imaging, it is attractive to explore the 3D capabilities of SEMs. This paper presents a comprehensive study of depth estimation performance when single- or multi-angle data is available. The research starts with an analytical line-scan model to show the major contributors of the signal change with increasing height and angle. We also analyze Monte-Carlo scattering simulations for height sensitivity on similar structures. Next, we validate the depth estimation performance with a supervised machine learning model and show correlation with the previous studies. As predicted by the sensitivity studies, we show that the height prediction greatly improves with increasing tilt angle. Even with a small angle of 3 degrees, a threefold average performance improvement is obtained (RMSE of 16.06 nm to 5.28 nm). Finally, we discuss a preliminary proof-of-concept of a self-supervised algorithm, where no ground-truth data is needed anymore for height retrieval. With this work we show that a data-driven tilted-beam approach is a leap forward in accurate height prediction for the semiconductor industry.
KEYWORDS: Data modeling, 3D modeling, Semiconducting wafers, Monte Carlo methods, Neural networks, Metrology, Semiconductors, Scanning electron microscopy, 3D metrology, Sensors
There is a growing need for accurate depth measurements of on-chip structures, fueled by the ongoing size reduction of integrated circuits. However, current metrology methods do not offer a satisfactory solution. As Critical Dimension Scanning Electron Microscopes (CD-SEMs) are already being used for fast and local 2D imaging, it would be beneficial to leverage the 3D information hidden in these images. In this paper, we present a method that can predict depth maps from top-down CD-SEM images. We demonstrate that the proposed neural network architecture, together with a tailored training procedure, leads to accurate depth predictions on synthetic and real experimental data. Our training procedure includes a domain adaptation step, which utilizes data from a different modality (scatterometry), in the absence of ground truth data in the experimental CD-SEM domain. The mean relative error of the proposed method is smaller than 6.2% on a contact-hole dataset of synthetic CD-SEM images with realistic noise levels. Furthermore, we show that the method performs well in terms of important semiconductor metrics. To the extent of our knowledge, we are the first to achieve accurate depth estimation results on experimental data, by combining data from the aforementioned modalities. We achieve a mean relative error smaller than 1%.
Background: Line-edge roughness (LER) is often measured from top-down critical dimension scanning electron microscope (CD-SEM) images. The true three-dimensional roughness profile of the sidewall is typically ignored in such analyses.
Aim: We study the response of a CD-SEM to sidewall roughness (SWR) by simulation.
Approach: We generate random rough lines and spaces, where the SWR is modeled by a known power spectral density. We then obtain corresponding CD-SEM images using a Monte Carlo electron scattering simulator. We find the measured LER from these images and compare it to the known input roughness.
Results: For isolated lines, the SEM measures the outermost extrusion of the rough sidewall. The result is that the measured LER is up to a factor of 2 less than the true on-wafer roughness. The effect can be modeled by making a top-down projection of the rough edge. Our model for isolated lines works fairly well for a dense grating of lines and spaces as long as the trench width exceeds the line height.
Conclusions: In order to obtain and compare accurate LER values, the projection effect of SWR needs to be taken into account.
Line-edge roughness (LER) is often measured from top-down critical dimension scanning electron microscope (CD-SEM) images. The true three-dimensional roughness profile of the sidewall is typically ignored in such analyses.
We study the response of a CD-SEM to sidewall roughness (SWR) by simulation. We generate random rough lines and spaces, where the SWR is modelled by a known power spectral density. We then obtain corresponding CD-SEM images using a Monte Carlo electron scattering simulator.
We find the measured LER from these images, and compare it to the known input roughness. We find that, for isolated lines, the SEM measures the outermost extrusion of the rough sidewall. The result is that the measured LER is up to a factor 2 less than the true on-wafer roughness. The effect can be accurately modelled by making a top-down projection of the rough edge. Our model for isolated lines works fairly well for a dense grating of lines and spaces, as long as the trench width exceeds the line height.
As device size continues to shrink, stochastic-induced roughness of resist features exposed by photolithography is of increasing concern to the semiconductor industry. In this paper, we propose an end-to-end approach for line roughness analysis by using the Line Roughness Module from our CDU solution family, which is a part of HMI’s metrology SEM tool the eP5. Simulated Scanning Electron Microscope (SEM) images of line/space patterns are used to verify the ability of the Module to reliably extract roughness related metrics. A set of imec EUV ADI images collected on our metrology SEM tool are analyzed by the Line Roughness Module, and wafer signature maps of various roughness metrics are obtained. These wafer maps not only help to analyze different roughness contribution sources, but also provide insights about feature roughness in a systematic way. Such information can be further used in a feedback loop to the scanner for model correction and process control.
Controlling currents using circularly polarized light and spin-orbit coupling could lead to the development of ultrafast spintronic devices driven by laser pulses and operating at the femtosecond timescale. Here we demonstrate that such a helicity dependent photocurrent can be generated in metallic heterostructures consisting of a single ferromagnetic layer and a non-magnetic one. In particular, using terahertz emission spectroscopy we show that the direction of the generated ultrafast photocurrent is controlled by the helicity of light, the magnetization of the ferromagnetic layer and the growth direction of the layers. We argue that the helicity and magnetization dependent photocurrent in metallic multilayers originates from a combination of the spin-orbit interaction and a lack of center of symmetry at the interface.
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