Before performing this analysis, (actual MONTE.SCP file),
you must first set appropriate tolerance values on instance and model
parameters using the part properties dialog for each part or class of
parts. Then you need to select the number of lots and cases to run from
the <Advanced\Monte Dialog>. Then make sure the Monte template
is selected, and the radio button above the question mark is selected.
The Data reduction should be set to Interactive and the Script checkbox
must be checked. Then Select one of the appropriate
Test Configurations and press Simulate Selections. Progress will be
shown in the IsSpice output window.

You can define the measurement results you want
to view either before or after running the analysis. Measurements defined
before running the analysis will be recorded in the database and can
be view using the <Results> button. The measurements are set up
for 5 sigma high values. Use the Set Limits dialog if you are establishing
measurement tolerances. The usual procedure is to leave the nominal
unchanged and choose expand to pass with symmetry to setup a symmetrical
tolerance band based on the Monte Carlo results.

A new dialog in IntuScope can be activated
after running a Monte Carlo analysis by pressing the <Monte>
button in the <Add Waveforms> dialog. To plot the statistical
results, you must have saved measurements in the "prob"
plot. You can add to the ones there using the <Monte Carlo>
dialog. Selecting vectors and functions from the appropriate
lists does this; then press the <Add Function to Plot>
button. Incidentially, you can plot all cases of each vector
by selecting a vector's x and y axis and pressing the <Plot
all Cases> button.

The really neat stuff happens when you
select a measurement from the "prob" plot and press
the plot group buttons. You get histograms or cumulative probability
plots scaled to your data set. Cumulative probability warps the
x axis into "sigmas" based on a normal distribution.
For small sample sizes, it is much easier to visualize the distribution.
We also plot the best fit of a straight line through these data
points. If the data is normally distributed, it will lay along
this straight line. The slope of the line, or first polynomial
coefficient is an estimate of the standard deviation. The rms
error is shown, and if it's small compared to the standard deviation,
you are probably looking at a normal distribution.

Next there is a feature to isolate the
data set that created each data point. Simply place cursor 0
on a data point and press the report button. It tells you which
simulation produced the data and shows each parameters value
and its sigma deviation. Using this feature, you can get an approximate
separation of class members for cases that have bi-modal distributions.
Then you can separate class members into several groups for more
detailed investigation.

Note: *To place cursor 0 exactly on a
data point, place it to the left of the point and with a blank
accumulator, press the "**0->Y" button in the Cursor control toolbar. The
cursor advance to the next data point each time you press the
button. Using the "Y<-0" marches the cursor backward along data points.*

Finally, you can substitute the values
associated with one of the simulations back into the schematic
to evaluate a "worst case" or re-center the design.