Success Story | GETECH Wafer Automatic Visual Inspector Helps Leading Semiconductor Silicon Wafer Manufacturer Achieve Automated Outgoing Quality Inspection
Inconsistent Standards, Low Efficiency: Inconsistent manual judgment standards lead to low inspection efficiency, struggling to meet high-capacity demands.
High Labor Dependency, Costly: High turnover among quality inspectors and lengthy training cycles of 3-6 months result in high labor costs.
Insufficient Precision, Limited Yield: The human eye can only identify defects down to approximately 5μm, unable to meet high-precision process requirements of 0.3μm-level.
Lack of Data, Difficult Traceability: Absence of systematic defect records makes historical data untraceable, hindering quality analysis.
Numerous Defects, Root Cause Hard to Identify: With many defect types (over 20 common types), manual statistics make data quantification difficult, preventing root cause analysis.
Challenging Real-Time Monitoring: Lack of real-time data feedback makes it difficult to promptly identify process anomalies, often leading to batch quality issues.
Addressing the characteristics of semiconductor silicon wafer production and high-precision detection requirements, GETECH launched the ALIOTH series S800F Automatic Visual Inspector. Designed specifically for defect detection on epitaxial and polished wafers, it utilizes CV (Computer Vision) + AI composite algorithms to achieve automated defect detection, classification, and statistics, comprehensively enhancing quality control capabilities.
01 High-Precision Appearance Defect Detection
Utilizes a high-resolution machine vision system to achieve 0.3μm-level detection accuracy, precisely locating and capturing surface defects.
- Features proprietary optical system design (multi-light source combination + polarized light adjustment), significantly improving the detection rate for challenging defects like slip lines, orange peel, and sintering (over 30% improvement compared to traditional equipment).
- Supports automatic defect classification (covers 20+ common types) with classification accuracy exceeding 95%.
02 Full-Process Data Statistics and Analysis
Real-time statistics on defect classification ratios and location distribution, generating visual reports to quickly identify areas for process improvement.
Batch analysis of defect trend changes, tracing defects back to specific equipment/process steps, and real-time monitoring of equipment performance fluctuations.
Conducts root cause analysis of defects combined with production data, providing data support for process optimization (e.g., polishing parameter adjustment, cleaning process improvement).
03 Adaptive Defect Recognition Capability
Adapts to different wafer specifications (4-12 inch) through adaptive imaging configuration (automatic brightness/focus adjustment) and multi-mode imaging (bright field/dark field/fluorescence).
Equipped with unsupervised transfer learning AI algorithms, automatically identifying new defect types without manual labeling, shortening response time for new defect detection (from the traditional 2 weeks to 1 day).
Interfaces with equipment control systems, automatically triggering alarms when defect rates exceed thresholds, pinpointing process anomalies.
By deploying the ALIOTH-S800F Automatic Visual Inspector, the customer achieved full automation of the outgoing quality inspection process, replacing traditional manual visual inspection, and achieved significant improvements in efficiency, accuracy, and yield:
Significantly Reduced Labor Costs
Quality inspection manpower per production line reduced by 60%. Equipment inspection speed reaches 30 seconds per wafer, supporting 24/7 operation to meet high-capacity demands.
Improved Detection Accuracy and Consistency
Overall defect detection accuracy reached 99%, with detection rates for critical defects (e.g., chipping, cracking) at 99.9% and for severity defects (e.g., polishing marks, slip lines) above 95%. Unified inspection standards eliminated human subjective error.
Significantly Improved Outgoing Yield
Process optimization through defect root cause analysis (e.g., adjusting polishing pressure, improving cleaning solution ratio) led to a marked increase in outgoing yield, reducing annual loss from defects by over ten million RMB.
Established Data-Driven Quality Management System
Achieved complete archiving and traceability of defect data, cumulatively storing defect data for over 1 million wafers, providing solid support for continuous process optimization.
