Editorial Feature

Detecting Defects in Microelectronics Using Particle Analysis

This article discusses using particle size analysis techniques to detect defects in silicon carbide (SiC), a wide-bandgap semiconductor used in microelectronic devices.

Detecting Defects in Microelectronics Using Particle Analysis

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Importance of Defect Detection in SiC

SiC is extensively used in microelectronic devices owing to its several unique properties. However, low yield and high cost of the SiC manufacturing process are the major challenges that must be addressed to realize mass production of high-quality SiC devices.

The performance of SiC devices is influenced by killer defects that form during the crystal growth process. Thus, improvements in the crystal growth techniques are crucial to reduce the defect density. Additionally, the use of post-growth inspection techniques in the manufacturing process is also necessary to detect and locate defects.

Major Defects in SiC

Crystallographic defects within the SiC wafer and surface defects near or at the wafer surface are the major defects in SiC. Crystallographic defects include grain boundaries, micropipes, threading screw dislocations (TSDs), threading edge dislocations (TEDs), stacking faults, and Basal plane dislocations (BPDs).

In SiC, epitaxial layer growth parameters are extremely critical for wafer quality. Crystallographic contaminations and defects during growth processes can extend to the wafer surface and epitaxial layer to form different surface defects, including scratches, polytype inclusions, and carrot defects, leading to adverse effects on the SiC devices.

Effect of Defects on SiC Device Performance

Defects affect the wafer quality and deteriorate the device performance fabricated on the wafer. For instance, micropipes increase the leakage current and limit the operation current, while polytype inclusions and carrots reduce the blocking voltage. Similarly, surface scratches can lead to reliability issues.

Point defects in SiC can decrease the device carrier lifetime, leading to lower breakdown voltages due to junction leakage currents. The effect of these defects can be reduced by optimizing the manufacturing process using accurate and fast defect inspection techniques.

Defect Detection Using Image Particle Analysis

Particle size analysis can be performed to characterize the particle size distribution in a sample. In several industries, particle size analysis controls quality and determines the manufacturing process efficiency and final product performance.

Image particle analysis is a particle size analysis technique that generates data by capturing images of every particle. The analysis can offer extremely high resolution and sensitivity. Transmission electron microscopy (TEM) and optical microscopy are primarily used for image particle analysis.


TEM can be employed to observe the sample subsurface structure at a nanoscale resolution. Electrons with high energy and ultra-short wavelength pass through the SiC sample surface, which is elastically scattered from the subsurface structure.

In SiC, crystallographic defects, including stacking faults, TSDs, and BPDs, can be detected using TEM. A scanning transmission electron microscope (STEM) can also be used for defect detection in SiC. STEM obtains atomic-level resolution through high-angle annular dark-field imaging (HAADF).

In 3C-SiC, partial dislocations and a trapezoidal stacking fault have been distinctly identified in TEM images, while three kinds of stacking faults consisting of three, two, and one faulted atomic layers have been detected in HAADF-STEM images.

Although TEM can be an effective SiC defect detection tool, using this tool to detect defects in a whole SiC wafer can be very time-consuming as it provides only one cross-sectional view at a time.

Additionally, the TEM mechanism requires an extremely thin sample with less than one μm thickness, which makes the sample preparation both challenging and time-consuming. Thus, TEM is not suitable as a practical tool for in-line or large-scale inspection.

Optical Microscopy

Optical microscopy can be employed to detect surface defects in SiC. Images can be produced in phase mode, bright-field mode, and dark-field mode using this technique. Images produced in a single mode provide specific defect information and most of the surface defects can be detected by combining images produced in all modes.

The dark-field mode captures the scattered light by surface defects when the inspection light illuminates the SiC surface. Thus, the image has a dark background, excluding the unscattered light and bright objects that indicate the defect location.

Unlike the dark-field mode, the bright-field mode captures the unscattered light and shows a white background image with dark objects owing to the scattering of defects. The phase mode captures images with phase shifts that are accumulated by the contamination on the SiC wafer surface and displays a phase-contrast image.

In optical microscopy, the scattering image is advantageous in lateral resolution, while the phase-contrast image primarily determines the wafer surface smoothness. Several studies have demonstrated the feasibility of using optical microscopy to identify surface defects. For instance, extremely thin micropipe or carrots defects can be detected using optical microscopy due to its lateral resolution advantages.

To summarize, particle analysis techniques can be used for effective defect detection in the field of microelectronics to improve the operating performance of microelectronic devices.

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References and Further Reading

Chen, P.-C., Miao, W. –C., Ahmed, T., Pan, Y. –Y., Lin, C. –L., Chen, S. –C., Kuo, H. –C., Tsui, B.-Y., Lien, D. -H. (2022). Defect Inspection Techniques in SiC. Nanoscale Research Letters, 17, p. 30. https://link.springer.com/article/10.1186/s11671-022-03672-w

Marinescu, I. D., Rowe, W. B., Dimitrov, B., Ohmori, H. (2013). 7 - Molecular dynamics for nano-contact simulation. Tribology of Abrasive Machining Processes (Second Edition), pp. 185-212. https://www.sciencedirect.com/science/article/abs/pii/B9781437734676000070?via%3Dihub

Mckenzie, S (2018). Particle Size Analysis Techniques [Online] Available at https://www.news-medical.net/life-sciences/Particle-Size-Analysis-Techniques.aspx 

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Samudrapom Dam

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Samudrapom Dam

Samudrapom Dam is a freelance scientific and business writer based in Kolkata, India. He has been writing articles related to business and scientific topics for more than one and a half years. He has extensive experience in writing about advanced technologies, information technology, machinery, metals and metal products, clean technologies, finance and banking, automotive, household products, and the aerospace industry. He is passionate about the latest developments in advanced technologies, the ways these developments can be implemented in a real-world situation, and how these developments can positively impact common people.


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