I assume you're talking about Don O'Riordan's note
http://www.designers-guide.com/Modeling/montecarlo.pdf?

If so, sigma is standard deviation. Process distributions consist of both a mean value and a standard deviation of course.

I'm not really sure what (if anything) you're asking.

In general, this information needs to be provided by the process foundry. Ideally they would provide statistics blocks with the model files, but it doesn't always happen...

Regards,

Andrew.

Apologies for the double post - I got a server error part way through posting, and re-posted before I checked to see if it had posted already  :'(

Hi Vikas,

This is quite clearly outlined in Don's paper. You need to make sure you have something like this:

Code:

inline subckt mynmos d g s b
parameters w=1u l=1u ad as pd ps ...
mynmos (d g s b) mynmosMod w=w l=l ad=ad ...

// parameterised model
model mynmosMod bsim3v3 type=n param1=value1 param2=value3 ...

ends mynmos
 

The idea is that you presumably have some parameterised model, with a statistics block describing the std deviation and distribution type of the parameters referred to in the model. What you then do is surround this with a subckt definition (it doesn't have to be inline; that just helps with backannotation), and then you can use the "model" mynmos wherever you like, as if it were a model. Because the real model (mynmosInt) is a parameterised model within a subckt, each instance has its own model, and so they will not be shared. If all instances are shared, you can't have mismatch - whereas if all models are unique to the instance, they can have mismatch.

Does that help?

Regards,

Andrew.