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Monte Carlo Analysis
Model-Based Monte Carlo Simulation
• The SSD approach is best suited to circuits where the CPU-time is a strong constraint for
the designer and specifically for applications with a very limited budget of simulations.
A typical sample size ranges from a few tens to hundreds of simulations. The linear
approximation used within the SSD algorithm assumes that the regression function is
linear in the inputs. Augmenting the number of runs does not automatically improve the
accuracy of the model. However, for prediction purposes, it can sometimes outperform
more complex non-linear models, especially in situations with small numbers of training
cases.
• You should first validate the SSD results against a standard Monte Carlo simulation
before applying the SSD method full-scale across a set of circuit variants.
• A non-linear model better takes into account the potential non-linearity of the circuit
responses. This compensates the rigid form of the linear models, and produce more
accurate models, leading to more accurate variability estimates. You should not expect
an accurate estimation of the output response in the tails. The SSD sampling plan is
designed to capture the so-called “normal events.”
• When the simulation budget ranges from a few hundreds or thousands of runs, the SSD
approach should not be considered as an alternative to the standard Monte Carlo.
Related Topics
Sampling Plan Methods
Model-Based Approximations Example
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Eldo® User's Manual, 15.3