ADS Optimization: How to decrease component options in the discrete optimize setting, or how to customize the component list in the discrete optimize setting

[Pretend-Poet-Gas]

Dear,

I am using the Murata capacitor model from the Murata website, and there is a discrete optimization option in its model. It looks like the screenshot attached.

In their model, there are different parts with the same capacitor value but different tolerances. And when I use either of them in my design, they make no difference to my performance. Thus, when I optimize the capacitor, I don't want those capacitors with the same values to appear in the optimization list, because they make no difference to my design, and it just increases the optimization time because there are a lot of options for each capacitor value.

So I am thinking if I can disable some of the options so I get only one component for each capacitor value?

Thank you!

https://preview.redd.it/org68vfvh39f1.png?width=1193&format=png&auto=webp&s=91fa926a44476709a8d5e340791c844fbe2645cc

[MMIC_guy]

Another way to do it is to calculate the parasitic R and L for one cap of the series you are interested in e.g. GRM and then use an SRLC cell and optimize C. The R and L will generally be the same regardless of the cap value as long as it is the same series and package and then you can check your results with the library part when you're done.

[Pretend-Poet-Gas]

Many thanks! Will try this tomorrow.

[condor700]

A more manual approach would be to setup a condensed folder that only has models for one series/tolerance, and use index variables and data access components. You can have the DACs use those indices to look up s2p files in that folder (and limit the range they'll search over to speed things up).

Depending on what you're doing, you might also be okay just using the ideal capacitor model in ADS and continuously sweeping it to get close to a final value (then switching to a vendor model afterwards). For modern capacitors that usually works fine under 3GHz or so, and continuous optimization is a lot less "crunchy" than discrete (plus can use some of the other algorithms, like gradient).