Depth and Shape estimation from focus in scanning electron microscope for micromanipulation

Marturi, Naresh; Dembélé, Sounkalo; Piat, Nadine

doi:10.1109/care.2013.6733694

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…These types of methods are commonly seen in optical microscopy where the DOF is small when compared to the SEM at specific magnification [11]. Using SEM, the manipulation tasks are often performed at higher magnifications (> 1000×) where the DOF is small enough to make depth measurements [9,12,13]. Besides, the computational speed and simplicity makes the focus-based methods more suitable for fast depth measurements during manipulation tasks.…”

Section: Introductionmentioning

confidence: 99%

Visual Servoing-Based Depth-Estimation Technique for Manipulation Inside SEM

Marturi¹,

Tamadazte²,

Dembélé³

et al. 2016

IEEE Trans. Instrum. Meas.

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Depth estimation for micro-nanomanipulation inside a scanning electron microscope (SEM) is always a major concern. So far in the literature, various methods have been proposed based on stereoscopic imaging. Most of them require external hardware unit or manual interaction during the process. In this paper, solely relying on image sharpness information, we present a new technique to estimate the depth in real-time. To improve the accuracy as well as the rapidity of the method, we consider that both autofocus and depth estimation as visual servoing paradigms. The major flexibility of the method lies in its ability to compute the focus position and the depth using only the acquired image information i.e., sharpness. The feasibility of the method is shown by performing various ground truth experiments: autofocus achievements, depth estimation, focus-based nanomanipulator depth control and sample topographic estimation at different scenarios inside the vacuum chamber of a tungsten gun SEM. The obtained results demonstrate the accuracy, rapidity and efficiency of the developed method.

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Section: Introductionmentioning

confidence: 99%

Visual Servoing-Based Depth-Estimation Technique for Manipulation Inside SEM

Marturi¹,

Tamadazte²,

Dembélé³

et al. 2016

IEEE Trans. Instrum. Meas.

Self Cite

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“…However, these methods are constrained to using smooth surfaces only. Another approach is to exploit the focus change of the electron beam, 13 , 14 similar to the depth of field effect in optical systems. Unfortunately, this technique is only feasible for the micrometer range and insufficient for most current applications in the semiconductor industry.…”

Section: Related Workmentioning

confidence: 99%

Training procedure for scanning electron microscope 3D surface reconstruction using unsupervised domain adaptation with simulated data

Houben

Huisman

Pisarenco

et al. 2023

J. Micro/Nanopattern. Mats. Metro.

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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 R 2 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.

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“…One way to improve the accuracy of the height prediction is to add variability to the data acquisition process when acquiring multiple images under different conditions. There are four ways to do this, as identified in the literature, by changing: 1) the incident angle, 2-4 2) landing energy, 5 3) beam focus, 6 or 4) detector position. 7 In this work, we will focus on the first method, changing the angle of the electron incident beam relative to the wafer.…”

Section: Introductionmentioning

confidence: 99%

Depth estimation from SEM images using deep learning and angular data diversity

Houben

Pisarenco

Huisman

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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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.

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Depth and Shape estimation from focus in scanning electron microscope for micromanipulation

Cited by 7 publications

References 10 publications

Visual Servoing-Based Depth-Estimation Technique for Manipulation Inside SEM

Visual Servoing-Based Depth-Estimation Technique for Manipulation Inside SEM

Training procedure for scanning electron microscope 3D surface reconstruction using unsupervised domain adaptation with simulated data

Depth estimation from SEM images using deep learning and angular data diversity

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