monte carlo simulation for yield

**Monte Carlo Simulation for Yield** is the **use of random sampling methods to model the statistical distribution of semiconductor yield** — simulating thousands of virtual wafers with random variations in defect placement, process parameters, and device characteristics to predict yield distributions. **How Monte Carlo Yield Simulation Works** - **Random Defects**: Scatter random defects across a virtual wafer according to defect density models. - **Kill Analysis**: Determine which defects land on active circuitry and kill the die. - **Process Variation**: Add random process parameter variations (CD, thickness, doping) sampled from measured distributions. - **Device Simulation**: Evaluate whether each virtual die meets electrical specifications. **Why It Matters** - **Yield Distribution**: Predict the full yield distribution (mean, variance, tail risk), not just the average. - **Design-Process Interaction**: Evaluate how design choices affect yield under realistic process variation. - **Risk Assessment**: Quantify the probability of yield falling below profitability thresholds. **Monte Carlo for Yield** is **rolling the dice thousands of times** — using random sampling to predict the full statistical distribution of semiconductor yield.