Glass substrate and TGV
Glass substrates are essential in advanced semiconductor packaging, including glass interposers with fine-pitch, high-aspect-ratio TGVs and glass cores with larger vias in thicker glass layers. Accurate inspection of these structures is critical to ensure product reliability. However, conventional tools often struggle to inspect internal features deeply buried inside the glass — especially tiny cracks or rough surfaces that may lead to failures. Holotomography captures the full 3D structure of vias and surrounding glass non-destructively. It can visualize internal features, sidewalls, subsurface cracks, and even provide cross-sectional images similar to observing a physical cut, without damaging the sample — regardless of via size, depth, or glass thickness (up to 2 mm).
Features
Discover Glass substrate and TGV with HT
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Laser Modification Analysis
Holotomography non-destructively maps laser-induced RI changes and micro-cracks inside glass for optimized TGV formation.
• Visualizes laser-modified regions inside glass in high-resolution 3D
• Quantifies minute refractive index changes caused by laser processing
• Detects and characterizes internal micro-cracks before selective etching
• Supports optimization of laser parameters for stable TGV formation
• Replaces destructive cross-section analysis with rapid, spatial 3D data
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Optimized Etching Process Control
Holotomography enables fast, non-destructive 3D analysis of etched TGV structures to optimize via geometry, sidewall quality, and process stability.
• Measures CD (Critical Dimension), taper angle, and full 3D internal profile
• Verifies structural uniformity across glass-core substrates
• Detects etching residues, sidewall roughness variations, and micro-defects
• Reduces reliance on destructive cross-sectioning for process validation
• Supports rapid calibration of etchant concentration and etching time
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Enhanced AOI Inspection Support
Holotomography adds high-resolution 3D validation to AOI defect candidates, reducing false calls and overkill.
• Provides 3D confirmation of defect candidates
• Classifies voids, micro-cracks, and internal defects with sub-micron spatial data
• Reduces false calls/overkill by distinguishing real defects from AOI artifacts
• Replaces destructive cross-sectioning with non-destructive defect validation
• Supports root cause analysis and process optimization