Source code for

**Design automation module**

Provides functions and classes for computing sensitivity, 
sizing a design across corners, worst case performance, 
worst case distance, yield targeting, and Monte Carlo analysis.  

from scipy.special import erf, erfinv
from numpy import ceil

[docs]def wcd2yield(beta): """ Computes the yield that corresponds to the worst case distance *beta*. """ return 0.5*(1+erf(beta/2**0.5))
[docs]def yield2wcd(y): """ Computes the worst case distance that corresponds to yield *y*. """ return erfinv(2*y-1)*2**0.5
[docs]def yieldSigma(y, nSamples): """ Computes the standard deviation of estimated yield *y* computed with *nSamples* Monte Carlo samples. """ return (y*(1-y)/(nSamples-1))**0.5
[docs]def nSamples(y, deltaY, confidence=0.99): """ Computes the number of Monte Carlo samples needed for obtaining a yield estimate that is within +-*deltaY* of *y* with confidence level given by *confidence*. """ # TODO check vs. eq. (230) in graeb k_gamma=erfinv(confidence)*2**0.5 return ceil(y*(1-y)*k_gamma**2/deltaY**2)