sum of sampled_noise output spectrum, how to access the phase information?

How about taking the spectrum of the sampled noise and multiplying it by the transfer function of the FIR filter in the frequency domain?

Dear NewScreenName,

In addition to Frank's good suggestion, I would like to add a few comments if I may regarding your post and some of the observations in your post.

1. I think a little more background regarding the nature of the circuit subject to the FIR filter might allow for some more specific suggestions from the experts, such as Frank, who monitor this Forum. For example, is its output quantized or does it undergo quantization as part of your FIR filter? What generic type of circuit are you simulating? Is the follow-on FIR filter used as an anti-aliasing filter or representing some type of noise filter to provide a standards based measurement? Are your signals periodic?

2.

NewScreenName said:

Is that because the noise is not retained once electrical nets are assigned to variables within the verilog model?

Assigning an input signal (i.e., the terminal voltage of a verilog-A block) to an internal verilog-A signal will retain the terminal voltage - irrespective if it is noise. However, if you are averaging the terminal voltage over some period of time and it is zero-mean based noise, its average may be zero. Are you doing any averaging?

3.

NewScreenName said:

I see the output noise spectrum is not a complex number,

The computed noise spectrum can be a complex number. For example, the ViVA dft() function returns a real and imaginary component at each spectral frequency. When plotted in ViVA, it will display the magnitude. You can access the real and imaginary components. The psd() function, by its nature, is a real quantity.

4.

NewScreenName said:

Is there a way to have a meaningful sum of the output noise at different time points, like for example by accessing the actual output power spectrum with its phase information?

I'm not sure I understand what you mean by "time points" as computing a spectrum from a time domain simulation requires a finite time window of simulation data. Hence, computing a spectrum from "different timepoints" is not something I think I understand. You could use take the dft() over a number of time intervals separated in time if that is close to what you are looking to do. It will retain phase information as I noted in my item 3.

5.

NewScreenName said:

Are there maybe more suitable ways to simulate this? (transient noise would require me very long simulations, so would be the last option if possible

I really need to better understand the nature of your circuit and your goal to provide a suggestion that might be useful - sorry!

Shawn

Dear NewScreenName,

In addition to Frank's good suggestion, I would like to add a few comments if I may regarding your post and some of the observations in your post.

1. I think a little more background regarding the nature of the circuit subject to the FIR filter might allow for some more specific suggestions from the experts, such as Frank, who monitor this Forum. For example, is its output quantized or does it undergo quantization as part of your FIR filter? What generic type of circuit are you simulating? Is the follow-on FIR filter used as an anti-aliasing filter or representing some type of noise filter to provide a standards based measurement? Are your signals periodic?

2.

NewScreenName said:

Is that because the noise is not retained once electrical nets are assigned to variables within the verilog model?

Assigning an input signal (i.e., the terminal voltage of a verilog-A block) to an internal verilog-A signal will retain the terminal voltage - irrespective if it is noise. However, if you are averaging the terminal voltage over some period of time and it is zero-mean based noise, its average may be zero. Are you doing any averaging?

3.

NewScreenName said:

I see the output noise spectrum is not a complex number,

The computed noise spectrum can be a complex number. For example, the ViVA dft() function returns a real and imaginary component at each spectral frequency. When plotted in ViVA, it will display the magnitude. You can access the real and imaginary components. The psd() function, by its nature, is a real quantity.

4.

NewScreenName said:

Is there a way to have a meaningful sum of the output noise at different time points, like for example by accessing the actual output power spectrum with its phase information?

I'm not sure I understand what you mean by "time points" as computing a spectrum from a time domain simulation requires a finite time window of simulation data. Hence, computing a spectrum from "different timepoints" is not something I think I understand. You could use take the dft() over a number of time intervals separated in time if that is close to what you are looking to do. It will retain phase information as I noted in my item 3.

5.

NewScreenName said:

Are there maybe more suitable ways to simulate this? (transient noise would require me very long simulations, so would be the last option if possible

I really need to better understand the nature of your circuit and your goal to provide a suggestion that might be useful - sorry!

Shawn

Dear ShawnLogan,

Regarding your points:

1. For the background, I hope my reply to Frank gave enough?

2. the input signal of the Verilog-A model internally was not assigned to an internal signal (by signal I suppose you meant an electrical net, right?) but just to a real variable. This is why I wondered whether this approach would be correct for the pnoise small signal analysis? I suspect those real variables (not electrical) cannot retain noise?

3. How can I access that? When I do imag(getData("out" ?resultsDir ?result "pnoise_sample_pm1"))) for example, I will get a zero signal across all spectrum, which I would not really expect. Am I doing something wrong here?

4. I hope after giving more context it sounds clearer now? I run a pss simulation, of L periods of my ADC sampling frequency, and chose L time points where to compute the pnoise