Multi-fab, tier-one chip manufacturer supplying smartphones, automotive electronics, and high-performance computing markets; high-throughput operations with stringent yield and delivery commitments
Semiconductor design and fabrication—advanced nodes, dense patterning, photolithography, metrology, and tightly controlled cleanroom manufacturing
High-performance logic and mixed-signal ICs where sub-micron defects can trigger scrap, rework, and downstream supply disruptions
Replace manual inspection with a scalable, learning-based AI vision system accelerating inspections, raising detection accuracy, and delivering traceable insight across lines and fabs
Hours-long, station-bound reviews throttled takt time and delayed lot release
Sub-micron/low-contrast defects escaped detection, driving escapes and variability
Advanced, dense layouts exceeded human consistency limits, raising false passes
Missed defects inflated scrap, rework, and downstream warranty exposure
Manual inspections produced no structured data, heatmaps, or trend visibility
Frequent stops for review reduced OEE and line stability
Shift-to-shift inconsistency weakened repeatability and audit readiness
AI analyzes high-resolution wafer images in seconds, enabling inline decisions without interrupting throughput
Models detect and classify 30+ defect modes, including low-contrast and overlapping anomalies
Validated outcomes retrain models, steadily improving precision/recall across products and nodes
Annotated defect maps, confidence cues, and next-best actions standardize decisions and speed disposition
Lot/wafer metadata, timestamps, and heatmaps create full lineage for trend analysis and audits
Preprocessing and augmentation stabilize performance across dense geometries and illumination shifts
Trains on labeled wafer images to recognize microscopic defect patterns with high accuracy and continuous improvement
Perform rapid multi-class classification/localization, supporting diverse defect modes without slowing inspection throughput
Enhances contrast, reduces noise, and stabilizes illumination so subtle flaws stand out consistently across batches
Central, curated library of labeled examples and outcomes that powers retraining, trend analysis, and traceability
Clear, annotated results with confidence indicators enable quick review and action directly on the production floor
Inline AI inference removed bottlenecks and kept lots moving
Micro-defects flagged before downstream processing, reducing rework and escapes
Guided review minimized inspection-induced downtime across lines
Centralized records with wafer/lot metadata, heatmaps, and timelines
Standardized workflows and confidence cues reduced variability across shifts
Analytics exposed recurring patterns, enabling targeted parameter tuning
Micro-defects flagged at source, preventing downstream escapes and costly rework
Inline AI inference accelerates inspection, improving takt time and throughput
Traceable records, heatmaps, and trends enable faster process tuning
Quality issues contained early, reducing waste and warranty exposure
Standardized workflows and confidence cues reduce human variability
Centralized, timestamped defect histories simplify compliance reviews
You’re one step away from building great software. This case study will help you learn more about how Simform helps successful companies extend their tech teams.
Discover cutting-edge ideas and insights from the world of technology and business.
You’re one step away from building great software. This case study will help you learn more about how Simform helps successful companies extend their tech teams.
