Electron-beam metrology and inspection

Brünner, Matthias; Schmid, R.

doi:10.1016/0167-9317(87)90005-0

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…At present, mainstream technology for semiconductor defect detection are divided into optical and electron beam detection technologies. Although electron beam technology has high accuracy, its detection speed is too low to meet the high productivity requirements [5,6]. Optical detection technology can be divided into imaging detection technology and non-imaging detection technology [7].…”

Section: Instructionmentioning

confidence: 99%

Full-flow surface defect identification method based on spot scanning scattering for unpatterned wafer

Qu,

Zhou,

et al. 2024

J. Inst.

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As the critical dimensions of semiconductor manufacturing processes gradually decrease, the requirements for production yield management become increasingly stringent. During the manufacturing process, there are many different types of defects, such as micron-sized particles, millimeter-sized scratches, etc. Multiple categories and different scales bring great challenges to the detection and identification of defects. This paper provides a full-flow surface defect identification method based on spot scanning scattering for unpatterned wafers. First, an adaptive threshold method with dynamic kernel windows is used to perform line-by-line scanning inspection of the wafer Mercator image. The 3σ decision strategy is used to avoid the impact of defects on background estimation and to improve detection sensitivity. After morphological processing, connected domain analysis is performed to obtain the defect mask, and feature information such as the shape, size, and distribution of the defect is extracted. Finally, the defect identification is performed by rules based binning, and the identified defects are converted into wafer polar coordinate image for display and analysis. In the experiments, the proposed method is used to identify micron-scale particles as well as large scratches on the millimeter scale for SiC wafers. Relative to the actual production rate requirement of 20 wafers per hour, the analysis time for a 6-inch wafer is 24.4 s, which can meet the requirement. Meanwhile, the test results illustrate the effectiveness of the method. The proposed method is recommended for early-stage defect detection and identification of unpatterned wafers.

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Section: Instructionmentioning

confidence: 99%

Full-flow surface defect identification method based on spot scanning scattering for unpatterned wafer

Qu,

Zhou,

et al. 2024

J. Inst.

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“…The video signal which is generated by the scanning e-beam is stored in a digital memory or directly displayed on a screen forming an image or a linescan. Averaging method is applied to reduce noise [5,6]. Combined with the scanning speed adjustment and astigmatic correction during the process, the signal-to-noise ratio can be increased.…”

Section: M-sem Image Edge Pixel Coordinates Detectionmentioning

confidence: 99%

Micron and Nanometer Measuring Methods Based on Metrological Scanning Electron Microscope

Yin¹,

Chen²,

Xia³

et al. 2014

KEM

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Micron and nanometer dimensional metrology is an important part of integrated circuits manufacturing system and nanofabrication research work. Semiconductor device feature size has shrunk from the micron to dozen of nanometers. How to watch the micro structure clearly, identify its edge, and measure its dimension accurately are the major issues that need to be research. In this paper, a metrological scanning electron microscope (M-SEM) system with precision stage and laser interferometer is presented. Furthermore, this paper focuses on demonstrating a metrological SEM image edge detection algorithm which is the essential part of the metrological SEM system to realize the measurement of line width. The procedure of the algorithm is that SEM image is first analyzed by noise type according to the histogram characteristics. Then the image noise is reduced by specific filters. Furthermore, a specific interesting object image region is selected and pixel gray level values of the region are obtained by line scan. SEM image edges are determined by the characteristic of the pixel information in two possible cases. This algorithm preserves the edge details of the original image and detects the image edge automatically in an easy and fast way.

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Overlay measurement using the low voltage scanning electron microscope

Rosenfield¹,

Starikov²

1992

Microelectronic Engineering

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Electron-beam metrology and inspection

Cited by 6 publications

References 34 publications

Full-flow surface defect identification method based on spot scanning scattering for unpatterned wafer

Full-flow surface defect identification method based on spot scanning scattering for unpatterned wafer

Micron and Nanometer Measuring Methods Based on Metrological Scanning Electron Microscope

Overlay measurement using the low voltage scanning electron microscope

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