Real chip fabs (and superconducting-qubit foundries building the same way) predict die yield with a Poisson critical-area model. Point defects — dust, lithography voids, resist tears — land at random positions across the wafer at an average density D₀ (defects per cm²). A die only fails if a defect lands inside its critical area Acrit — the small fraction of the die (Josephson junctions, wiring pinch-points) actually sensitive to a defect:
A_crit = N_qubits · a_q (a_q ≈ 0.0015 cm² per qubit)
λ = D₀ · A_crit (expected defects on one die's critical zone)
P(die survives) = P(0 defects) = e^(−λ) ← Poisson probability of zero hits
This simulator scatters an actual Poisson-distributed set of defect points over the wafer (not just the formula) and tests each die's critical zone individually, so the measured yield you see is a real random sample — it converges to the theoretical e^(−λ) curve but jitters around it, exactly like a real wafer run.
- Defect density D₀ — process maturity. A mature fab runs low D₀; a new process runs high D₀.
- Die size — bigger dies pack fewer chips per wafer but change how the fixed-radius wafer edge is used.
- Qubits per die — more qubits per chip means more critical area, so λ grows and yield falls — the core scaling tension quantum-hardware roadmaps face: bigger chips ship more qubits per good die, but fewer dies survive.
- New Wafer — resamples the defect map for the current settings, showing run-to-run variance around the theoretical curve.
Real-world relevance: this is the same yield-vs-defect-density trade-off superconducting-qubit companies (IBM, Google, Rigetti) publish in their scaling roadmaps — it is one of the concrete engineering bottlenecks behind "more qubits" being harder than it sounds.