sheldon
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Emad,
Some additional comments:
1) LHS is only supported in recent versions of Spectre and IC5141, forget which release, contact your local support. They would know.
2) In good statisitical models, the distributions are chosen to match the effect to be modeled, usually parameters have gaussian distributions though some parameters use other distributions, for example Beta is often modeled with a log-normal distribution.
3) I would kind of go the other way on circuits passing corners and failing Monte Carlo. It seems to me that unless the definition of corners is fairly precise this will happen fairly often. That is, the digital concept of corners, Fast/Typical/Slow, does not make sense in the analog world. I think this is what Vivek is indirectly refering to when he talks about worst-case corners. Corners need to account for the impact of process variation on the analog characteristics of transistors.
4) From the design methodology view point, I don't think that corner analysis and Monte Carlo analysis are "competitors", they complement each other.
If you are interested in verifying that your design meets specification, then corner analysis is the appropriate tool for the job. If a corner fails and you want to assess the impact of the failure, then Monte Carlo is useful. It will give you insight into the yield impact of the failure.
If you are designing a circuit, then Monte Carlo analysis is good. It provides insight into the relationship between design parameters and specifications, correlation plots, and the design margin, how tight is the distribution and is it centered properly. This information is difficult to extract from corner analysis.
The challenge is as the number of process corners increases, is it more efficient to use corner analysis or just run Monte Carlo on everything. My feeling is that the answer to that question is specific to your project.
Last point, is there is lot you can do with statistical analysis, if you have good models. The challenge has always been, particularly for CMOS processes, getting good models, that is, models that accurately reflect process distributions.
Best Regards,
Sheldon
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