Monte Carlo analysis is a procedure to assess manufacturing yields by repeating simulation runs with varying applied random variations to component parameters. The technique is very powerful and usually gives a more realistic result than worst-case analysis which varies component values to their extremes in a manner which produces the worst possible result.
The implementation of Monte Carlo analysis in SIMetrix has been designed to be quick to set up for simple cases while still providing the required flexibility for more advanced requirements as might be required for integrated circuit design.
SIMetrix offers a level of flexibility for tolerance specification that cannot be found in other products including some high priced UNIX based applications. It is possible, for example, for different model parameters to be dependent on a single random variable. This makes it possible to model the fact that a number of model parameters might be dependent on a single physical characteristic, for example, the base width of a bipolar transistor. Of course, lot tolerances are also implemented accounting for the matching of devices in integrated circuits and other multiple components built onto a common substrate. However, in many products, lot tolerances can only be applied to the same type of device. In SIMetrix it is possible to model parametric relationships between different types of device which occur in integrated circuits but which are rarely taken into account.
As well as conventional multiple step Monte Carlo analysis, single step Monte Carlo sweeps may also be performed. These are available for the four swept modes, .AC, .DC, .NOISE and .TF. For example, a Monte Carlo analysis of the DC offset voltage of an amplifier can be performed using a single run of .DC using a special sweep mode. This is dramatically faster than the alternative of repeated .OP runs. This type of analysis can also be used to analyse the gain of an amplifier at a single frequency using .AC or .TF or even the noise, again at a single frequency, using .NOISE.
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