Monte Carlo simulaition with Tspice

  • Thread starter IanTrout
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    Monte carlo
In summary, you need to use the .param p=agauss(3n, 0.3n, 1) line to change the inductor value in a Monte Carlo simulation.
  • #1
IanTrout
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Hi,

I have a small problem with running a Monte Carlo simulation in an exercise I have.

We are using Tanner EDA to simulate our circuits, and we have to change the value on an inductor while running a Monte Carlo simulation.

Is there a way to do this? Whats the proper command to change that particular parameter?

Unfortunately, we don't really have any good references for this software, not from our instructor and not from the web...


-----EDIT-----
I didn't notice there way a separate forum for class work, sorry about that...

So its a question for the moderators, move this post or keep it here...
-----EDIT-----


Thanks in advance,
Ian Trout
 
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  • #2
IanTrout said:
Hi,

I have a small problem with running a Monte Carlo simulation in an exercise I have.

We are using Tanner EDA to simulate our circuits, and we have to change the value on an inductor while running a Monte Carlo simulation.

Is there a way to do this? Whats the proper command to change that particular parameter?

Unfortunately, we don't really have any good references for this software, not from our instructor and not from the web...


-----EDIT-----
I didn't notice there way a separate forum for class work, sorry about that...

So its a question for the moderators, move this post or keep it here...
-----EDIT-----


Thanks in advance,
Ian Trout

Your question is okay here for now. Does TSPICE at least have a step-plot option? Can you just step the values of inductor to obtain different simulation plots? Are you just varying the inductor, or do you want to do a Monte Carlo variation on multiple parts? If it's just the one component that varies, you don't really need a Monte Carlo feature...
 
  • #3
Thanks for the reply.
I dug a little deeper into the Tspice documentation and found the missing link...

For future references, one needs to use the following lines:

Code: 
.param p=agauss(3n, 0.3n, 1)
LL1 N_1 S2  L=p
.tran/Powerup 0.01n 100n Sweep MONTE=40


This example does a Monte Carlo sweep in transient mode, in each cycle it changes the parameter p that is assigned to the inductor value. One can add more parameters as needed.


Thanks,
Trout
 

Related to Monte Carlo simulaition with Tspice

1. How does Monte Carlo simulation work in Tspice?

Monte Carlo simulation in Tspice involves running multiple simulations with randomized input values to analyze the impact of process variations and parameter uncertainties on circuit performance. It uses statistical methods to determine the probability of different outcomes and provides a more realistic assessment of circuit behavior compared to deterministic simulations.

2. What are the benefits of using Monte Carlo simulation in Tspice?

Monte Carlo simulation in Tspice allows for a more comprehensive analysis of circuit performance by taking into account the effects of process variations and parameter uncertainties. It also helps identify potential design weaknesses and provides insights for improving circuit robustness.

3. How is Tspice's Monte Carlo simulation different from other simulation tools?

Tspice's Monte Carlo simulation is based on the industry-standard SPICE engine, making it highly accurate and reliable. It also offers advanced capabilities such as the ability to define custom distributions for input variables and perform sensitivity analysis.

4. Can Monte Carlo simulation in Tspice be used for all types of circuits?

Yes, Monte Carlo simulation in Tspice can be used for various types of circuits, including analog, digital, and mixed-signal circuits. It is particularly useful for complex and highly sensitive circuits that are prone to process variations and parameter uncertainties.

5. How can I interpret the results of Tspice's Monte Carlo simulation?

The results of Tspice's Monte Carlo simulation are typically presented in the form of histograms, scatter plots, and statistical summaries. These can help identify the variability in circuit performance and provide insights for optimizing the design. It is essential to understand the underlying statistical concepts to interpret the results accurately.

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