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Frank Thielert
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- Fast and with nanometer precision
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- High level of automation
confovis · Industries ·
The market for components consisting of power semiconductors (e.g. made of silicon carbide [SiC]) is growing, as is the case with the production of battery-powered cars and other applications.
At the same time, the use of SiC wafers presents new challenges for the automated optical power semiconductor inspection of the wafer surfaces. It is one of the biggest challenges for the SiC defect inspection to reliably inspect inhomogeneous surfaces during the process monitoring.
The Confovis WAFERinspect AOI allows a selection of areas for the high-resolution power semiconductor inspection (0.1 µm/pixel) with the help of Care Areas and the distinction of process errors caused by structure variations by means of artificial intelligence. Both feature-based classifiers that are trained by neural networks. Also Deep Learning Methods are available for this purpose.
The constantly increasing requirements concerning the components’ reliability (as is the case with autonomous driving, as an example) make an optical inspection necessary during the early process steps. The aim is to detect defects that have a negative impact on the components’ service life that cannot be identified during an electrical test.
The patented Confovis high-end optics and illumination strategy makes it possible to detect small defects with high resolution in power semiconductors. Irrespective of the surface’s contrast, the Confovis WAFERinspect AOI keeps the structure in focus which is to be inspected. It is therefore possible to use objective lenses with a high numerical aperture (NA), in order to benefit from maximum lateral resolution of light-optical inspection systems.
- Defect detection on electrodes with a rough surface occurring with materials such as gallium nitride (GaN), gallium arsenide (GaAs) and indium phosphide (InP)
- Inspection of transparent and semi-transparent wafers as well as epitaxial layers
- Fault classification by means of a feature-based neural network and Deep Learning
- Automated wafer handling also of thinned and framed wafers
- Most diverse analysis options, such as Golden Sample, Care Areas, image processing, classificators
- Inspection at several focus levels possible
- Patented confocal 3D-sensor with a unique accuracy
- Multi-frequency illumination and differential contrast
CONFOVIS WHITEPAPER & CASE STUDIES
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Challenges for the optical inspection of glass & compound semiconductor wafers
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Full 3D defect and anomaly inspection for inline semiconductor manufacturing
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Optical high-resolution image-based defect inspection on compound semiconductors
DOWNLOAD CASE STUDY
- Macro defects
- Micro defects
- Particle inspection
- Golden Sample
- Defect detection with AI (deep learning, anomaly detection)
- Feature-based defect classification with neural network
- Independent of materials: silicon, epoxy, glass, chrome, resist, etc.
- Measurements of demanding surfaces without artifacts (no ‘bat wings)
- High speed: 60 confocal frames per second (250 mil. measuring points)
- Film Thickness / Layer Stack
- Topography
- Coplanarity
- Bumps
- Roughness
- Step Height
Critical Dimensions
- Line/Space
- VIAs
- Oblong holes
- Overlay
The Confovis WAFERinspect AOI tool combines various inspection jobs on power semiconductors. By combining automated optical inspection (AOI) with metrology, Confovis AOI is able to offer solutions for the defect inspection and classification as well as 3D and 2D measurements (Wafer Level Metrology).
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