The following is a video clip from the webcast, "Four Ways to Boost Simulation Data Processing
Using Python".
In this segment, Matt Ozalas talks about how to compute circuit and model values prior
to running a simulation.
Matt shows us an example of how to synthesize an acoustic filter by using a residual extraction
technique.
All right.
So back to the presentation.
We've got three down and one to go.
In the previous three, I've really talked about how to post-process data.
This has been all about post-processing, right?
Basically, what we've done is taken the data that we have after simulation and manipulate
it into some manner.
Well, in this last example, we're going to do something a little bit different.
We're actually going to preprocess data.
So we're going to synthesize the filter before we run the simulation and get those filter
values in.
And the filter that I'm going to synthesize is an acoustic resonator filter.
So these are widely used in RF systems.
And generally speaking, if you told me to go design a filter, the first thing I would
probably think about doing is synthesizing it.
But for acoustic filters, it's really challenging to synthesize something.
I haven't seen it in any of the software packages out there, the ability to do that.
So, I spent a lot of time in Python and looked at some of the stuff in the literature and
basically wrote a Python script to synthesize a ladder filter.
And I actually had that before I built any of this data link stuff.
It was already kind of there.
And so, given that I've got this Python script, I want to show you how to tie it into the
simulation.
So I'm going to synthesize the filter and Python.
And how can we tie this together with the simulation so that the values from the filter
just preload right into the schematic and I can immediately validate if I've done a
synthesis step, whether or not my filter response agrees with what I thought it did from the
synthesis.
And just to give you a little bit of background on acoustic resonators, the challenge with
synthesizing them is really that they're not one element.
They're at least three elements.
And so it's usually a Series LC.
That's the acoustic part of it.
And then there's some kind of parasitic-like overlap capacitance.
And just digging in to that a little more, the Series LC, the mechanical or acoustic
elements give you this series resonance, and the parallel capacitance gives you a parallel
resonance.
And those happen pretty close in frequency.
So usually these are very, very high Q resonators.
And the way that they're built is by cascading sets of them together.
So we can create a ladder network by doing a series resonator and a shunt resonator.
And usually the shunt resonators are detuned in frequency.
So those set the lower band pass null and the series resonator will set the higher band
pass null.
So we can improve the rejection of the nulls by just adding more elements to the filter.
That's usually what's done.
The challenge is, so you can get great, here's the rejection that you get out
of the null.
So you can see the nulls are getting much, much deeper as we add more elements.
But the problem is, let's say I use the exact same element in series and then I use the
exact same elements in shunt.
If I did that, I would get a passband response that doesn't look great.
It ends up looking kind of ripply.
So it's not completely ideal.
So to achieve a nice passband response, the filter elements need to be tuned or optimized
or designed to get some kind of response.
So I don't have nearly enough time to cover all the details about this.
This is another area where I could spend the entire webcast just talking about this one
subject.
But there's a paper out there that I based this on.
And what I do is essentially obtain a characteristic Chebyshev polynomial with finite transmission
zeroes.
And so I can take the specs that someone gives me and generate this type of polynomial.
So I've done that for the input impedance here.
So the key is keeping the numerator and denominator s-domain polynomials intact.
And then I pass those s-domain polynomials into a separate function to generate the filter.
So I start with an ideal Chebyshev response.
Then I generate a realistic filter with these resonator components.
And basically what I do is I use this residual extraction technique where we look at the
filter response at each of the zeroes.
And when we do it in the right way, everything else just kind of disappears and we're able
to focus in on a particular resonator.
So the output of this Python script, it's really just we print out a few values here.
And that was the state that it was in.
So linking it to ADS, I actually start in the data display and I specify some filter
specs.
And then I'm going to pass those into Python and it's going to synthesize the filter.
And what it will return is the low-pass prototype response.
To it'll send me the frequency response of the low-pass prototype that it generates.
At the same time, it's also going to make a file.
So it'll do that in the background.
And this file is going to have the elements of each of the filter components in it.
And so I can access that in simulation using a data access component and just preload those
values directly into my simulation, so all I need to do is hit simulate and I have the
simulated response of the ladder network.
So, I'll give you a really quick demo of that, too.
So here's my schematic.
This is my ladder network.
And just to kind of push in to each one of the components, these all look the same and
they're just series resonance and a parallel resonance here.
And in this case, if I try to hit simulate, I'll get an error because the file doesn't
exist yet.
I haven't synthesized anything yet.
So I'm trying to read a file that doesn't exist.
But let me open the data display now.
So this is the Python script I'm going to use.
It's called Ladder Synthesis BVD.
And these are the specs.
So this is the low and high side of the band pass, and then these are the transmission
zeroes that I specify.
And by the way, I can specify more zeroes.
I just, in this case, did four of them.
And I'm also going to specify the return loss.
So let me do that.
And it's going to go in the background in turn and give me a prototype filter response.
So that's return directly from Python.
And you can see that this is an equiripple response.
And also it's low pass.
So this is not frequency scaled yet.
So when I go over to the simulator, what happened is, hopefully, this file was created.
And so when I hit "Simulate," now it simulates.
And that's it.
There's my response with all my resonators that have been synthesized at 2 gigahertz.
So this is the scaled version of this filter.
So I can sit here and now start evaluating the filter.
Rather than typing in values from the synthesis to the simulation, we can do that pre-computation
step and just have everything get populated into the schematic.
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