stratified sampling

**Stratified Sampling** is **a sampling method that selects observations proportionally or intentionally across predefined strata** - It is a core method in modern semiconductor statistical quality and control workflows. **What Is Stratified Sampling?** - **Definition**: a sampling method that selects observations proportionally or intentionally across predefined strata. - **Core Mechanism**: Each stratum is represented in the dataset so minority or high-risk groups are not overlooked. - **Operational Scope**: It is applied in semiconductor manufacturing operations to improve capability assessment, statistical monitoring, and sampling governance. - **Failure Modes**: Improper stratum weighting can bias conclusions and misallocate corrective actions. **Why Stratified Sampling Matters** - **Outcome Quality**: Better methods improve decision reliability, efficiency, and measurable impact. - **Risk Management**: Structured controls reduce instability, bias loops, and hidden failure modes. - **Operational Efficiency**: Well-calibrated methods lower rework and accelerate learning cycles. - **Strategic Alignment**: Clear metrics connect technical actions to business and sustainability goals. - **Scalable Deployment**: Robust approaches transfer effectively across domains and operating conditions. **How It Is Used in Practice** - **Method Selection**: Choose approaches by risk profile, implementation complexity, and measurable impact. - **Calibration**: Define sampling weights from objective production mix and risk priorities, then verify realized coverage. - **Validation**: Track objective metrics, compliance rates, and operational outcomes through recurring controlled reviews. Stratified Sampling is **a high-impact method for resilient semiconductor operations execution** - It improves representativeness and comparability across heterogeneous process populations.