Die yield calculator (defect density models)
Yield, quality and reliability. Everything is calculated in your browser; nothing you enter is sent to a server.
KEY POINTS
Yield loss from random defects is set by the product of defect density D0 and die area A. In the simplest Poisson model Y = e^(−AD0), so yield falls exponentially as area grows. Real defects cluster, so models such as Murphy's are used, but larger dies are always disproportionately penalized, which is the economic case for splitting designs into chiplets.
Inputs
The Poisson model assumes defects are completely random and tends to be pessimistic. The Murphy model allows for non-uniform defect density, is often closer to measured yield and is widely used. These yields cover random defects only, not design-related or parametric failures.
Splitting the die (the economics of chiplets)
| Number of dies | Area per die | Yield per die | All N dies good |
|---|---|---|---|
| 1 (monolithic) | 1.000 cm² | 90.5 % | 90.5 % |
| Split into 2 | 0.500 cm² | 95.1 % | 90.5 % |
| Split into 4 | 0.250 cm² | 97.5 % | 90.5 % |
Looking only at the chance that all N dies are good, splitting seems not to help. In real chiplet designs, however, only dies tested as good (known good die, KGD) are combined, so what matters is how little area is thrown away with each defective die. Splitting makes the loss per defective die smaller, which is the economic basis for chiplets.
FAQ
Should I use the Poisson or the Murphy model?
Poisson assumes completely random defects and tends to be pessimistic. Murphy accounts for uneven defect distribution, is closer to measured yields and is more widely used in industry.
Why do chiplets improve yield?
Yield falls exponentially with die area, so splitting one large die into several small ones raises the yield of each piece. Known good dies are then combined in the package, at the cost of extra packaging and interconnect.
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Last updated: October 23, 2026