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AC noise vs. transient noise power discrepancy

patt

I am trying to relate the results from an AC noise analysis to those from transient noise. For the purpose I have taken a single 1K resistor, grounded one terminal and left the other one floating. AC noise analysis gives the expected VN()=sqrt(4kTR) noise voltage spectral density (in volts per sqrt(Hz)) of the floating node. Integrating the noise power spectral density, i.e., iinteg(VN2()) would give me the total noise power across the evaluated frequency range.

Now I run a transient noise simulation of the same circuit across the same frequency range. Plotting dft(VT("/floating_node"),0,tran_length,2*max_freq,"Rectangular",1,1) yields a flat spectrum, presumably of the noise voltage integrated per bin of size 1/tran_length Hz. Squaring that, and summing it, i.e., iinteg(abs(dft(VT("/floating_node"),0,tran_length,2*max_freq,"Rectangular",1,1))**2)*tran_length (all right, I use iinteg() here to obtain a sum, which is why I divide by the bin size and accept the 50% error in the very first and very last data point) should again result in the total noise power. However, the number I get is quite different from the one obtained from the AC noise analysis above.

Any idea what I am doing wrong? How do I get the two figures to match?

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  • Hello Andrew,

    Thank you for your kind reply. I was rather hoping that in your infinite wisdom you would be able to suggest something..

    The integrated psd() gives the same result as the summed squared dft() (divided by 2, which I had overlooked previously, so there). Either way, the result is very much dependent on the (subset of the) time period simulated and the number of samples considered. I can get a result that corresponds to the theoretical PSD of, say, a resistor, but I could equally well get one that is a factor of 10 off. Perhaps there is something about the actual fft that I'm missing. Naturally, the power of 2 sampling rule has been complied with, but does not seem to make much difference.

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  • Hello Andrew,

    Thank you for your kind reply. I was rather hoping that in your infinite wisdom you would be able to suggest something..

    The integrated psd() gives the same result as the summed squared dft() (divided by 2, which I had overlooked previously, so there). Either way, the result is very much dependent on the (subset of the) time period simulated and the number of samples considered. I can get a result that corresponds to the theoretical PSD of, say, a resistor, but I could equally well get one that is a factor of 10 off. Perhaps there is something about the actual fft that I'm missing. Naturally, the power of 2 sampling rule has been complied with, but does not seem to make much difference.

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