Solutions by Industry
Semiconductor Industry
Where small deviations decide yield
Questions this page answers
- How do you identify yield-affecting factors in semiconductor process data?
- Which signals reveal equipment anomalies early?
- How do you process high-volume metrology data in real time?
Requirements in this industry
- Yield
- Metrology data
- FDC (Fault Detection & Classification)
- Process deviation
- Anomaly detection
- SPC
More data does not mean more answers
Semiconductor processes do not suffer from a shortage of data. If anything there is so much metrology and sensor data that deciding where to look is the harder problem.
The variables affecting yield are numerous, and a variable that looks insignificant on its own can cause trouble when it coincides with another condition. Interactions like that rarely surface in a spreadsheet review.
Approach
Collection structure first — Given the volume and velocity, the collection and storage design comes first. If that is shaky, the analysis behind it means little.
Variable influence analysis — Places process variables alongside outcome metrics to see which combinations lead to deviation.
Early equipment anomaly detection — Finds patterns in equipment signals that differ from the norm and raises them before failure.
Visual inspection — Deep learning inspection (V²) supplements rule-based vision where judgment is difficult.
Solutions applied
- B²LABA big data platform that optimizes manufacturing equipment as the base for AI
- A²Autonomous × A.I — AI visual analytics and visualization
- V²Vision × Validation — automatic detection of fine defects with deep learning
- Data StandardizationManufacturing data standardization that builds an interoperable ecosystem