Monte Carlo Simulation for a CMOS based circuit

Thread Starter

Harsha Vardhan Y

Joined Aug 25, 2023
  1. General Approach:
    • Can someone provide a step-by-step guide on setting up a Monte Carlo Simulation for a CMOS circuit in Cadence Virtuoso?
    • What are the key considerations when planning Monte Carlo Simulations for CMOS circuits?
  2. Parameter Selection:
    • Which parameters in a CMOS circuit are typically chosen for Monte Carlo analysis?
    • How do I determine the appropriate parameter ranges or distributions for Monte Carlo simulations in Virtuoso?

  1. Resources and References:
    • Can anyone recommend textbooks, tutorials, or online resources that provide in-depth guidance on Monte Carlo simulations in Virtuoso for CMOS circuits?
    • Are there any research papers or articles that discuss practical applications of Monte Carlo analysis in VLSI design?
  2. Experiences and Tips:
    • Do you have any personal experiences or tips to share when conducting Monte Carlo simulations for CMOS circuits in Virtuoso?


Joined Sep 9, 2010
I cannot comment on your software or CMOS, but only on the principles of a Monte Carlo simulation. Sorry if this is already obvious to you.

In many fields, you try to describe a system with a model, an analytical calculator that calculates output(s) or dependent variables, given a set of inputs, or independent variables. It's often the case that you can't define the precise value of each independent variable but can only guesstimate a mean and variance of that variable. For instance in an economic model you might fix the Fed funds rate, since it is known, but allow the inflation rate to be in some range, since it is not known beforehand.

Any independent variable that cannot be assigned a fixed value can instead be represented by a bell curve, a Gaussian distribution with a mean and standard deviation. You run the model through many, many (typically thousands) of iterations while allowing the value of each input variable to be drawn from its distribution. In this manner, multiple input distributions are considered and work together to produce the expected distribution of outcomes.

A serious limitation on the approach is that the individual input distributions may not be well known. You can guess at the mean and standard deviation for the inflation rate, but past data may not be predictive of the future. And then there are Black Swans to consider.

All models are wrong, some are useful - Box