classification for binning

**Classification for Binning** is the **application of ML classification algorithms to sort finished chips into performance bins** — predicting whether a die will be fast, typical, or slow based on inline process measurements, enabling early yield prediction and optimized testing strategies. **How Is It Applied?** - **Features**: Inline metrology (CD, thickness, overlay), process tool data, wafer position. - **Labels**: Final electrical test bin assignments (speed grades, pass/fail). - **Models**: Random forests, gradient boosting, neural networks trained on historical data. - **Prediction**: Predict bin assignment from inline data before final test — enables sort/test optimization. **Why It Matters** - **Test Time Reduction**: Pre-classify wafers to focus expensive testing on borderline cases. - **Yield Prediction**: Predict yield and bin distribution before wafers reach final test. - **Revenue Optimization**: Earlier bin prediction enables better production planning and customer allocation. **Classification for Binning** is **predicting chip performance from process data** — using ML to sort dies before they reach the tester.