decision trees for root cause

Decision Trees for Root Cause Analysis is the application of decision tree algorithms to identify which process conditions split wafers into good and bad groups — producing human-readable, interpretable rules that manufacturing engineers can directly use for root cause investigation.

How Are Decision Trees Used?

• Features: Process parameters from equipment data (chamber, recipe, gas flows, temperatures).
• Labels: Pass/fail or yield categories from downstream measurements.
• Tree Structure: Each node splits on the most discriminating variable — the path from root to leaf is an if-then rule.
• Pruning: Control tree depth to prevent overfitting while maintaining interpretability.

Why It Matters

• Interpretability: Unlike black-box models, decision trees produce human-readable rules (e.g., "IF chamber B AND temperature > 405°C THEN yield < 90%").
• Variable Ranking: Variables appearing near the root are the most important discriminators.
• Fast Investigation: Engineers can immediately test the identified conditions and verify the root cause.

Decision Trees are the automated detective for fab problems — finding the simplest set of process conditions that separate good wafers from bad.