Semiconductor Industry

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.

Production record structure — The general manufacturing MES core keeps LOT tracking and equipment utilization. However much measurement data there is, it does not carry through to yield analysis unless each value connects back to a LOT and a machine.

Visual inspection — Deep learning inspection (V²) supplements rule-based vision where judgment is difficult.