The K-Sigma Corners Method
This method creates the K-Sigma statistical corner that meets the specified target yield value and applies the stopping criteria according to which the Monte Carlo simulation is stopped when the K-Sigma statistical corner has been created for each specification.
It is possible that multiple corners meet the target specification criteria. Therefore, the K-Sigma method finds the most representative corner by calculating the minimum distance to the nominal point. This representative corner has a greater probability to occur. The statistical corner can then be used for further analysis of the design.
The fast K-Sigma corner algorithm estimates the Probability Density Function (PDF) of the performance distribution maintaining the accuracy of non-normal distributions. The specification target value is computed from the PDF estimate. Corners are generated based on the modeling and extrapolation from the PDF of the output.
This method is recommended if you prefer speed to accuracy.
useDoubleSidedSigma variable is set to nil. Therefore, yield is represented as probability integration from -infinity to +K sigma in Gaussian distribution, and thus, 3 sigma is converted to 99.865%. When this variable is set to t, probability integration is done from -K sigma to +K sigma, and thus, 3 sigma is converted to 99.73%.Related Topics
Workflow of the K-Sigma Corners Method
Running the K-Sigma Corners Method
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