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.