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%.
The Fourier modal method (FMM), also referred to as Rigorous Coupled-Wave Analysis (RCWA), is based on Fourier-mode expansions and is inherently built for periodic structures such as diffraction gratings. When the infinite periodicity assumption is not realistic, the finiteness of the structure has to be incorporated into the model. In this paper we discuss the recent extensions of the FMM for finite structures. First, we explain how an efficient FMM-based method for finite structures is obtained by a reformulation of the governing equations and incorporation of perfectly matched layers (PMLs). Then we show that the computational cost of the method can be further reduced by employing an alternative discretization instead of the classical one. Numerical results demonstrate the characteristics of the discussed FMM-based methods and include a discussion of computational complexities.
This paper extends the area of application of the Fourier modal method from periodic structures to aperiodic
ones, in particular for plane-wave illumination at arbitrary angles. This is achieved by placing perfectly matched
layers at the lateral sides of the computational domain and reformulating the governing equations in terms of a
contrast field which does not contain the incoming field.
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