How to view spectrum with an oscilloscope?

Spectrum analysis is a common method of signal analysis. Through frequency domain analysis, many signal problems that are unclear or ambiguous in the time domain can be discovered. Oscilloscopes are the most traditional time domain analysis tools, but with the help of mathematical functions such as FFT, oscilloscopes can also perform spectrum analysis of signals, thus providing more powerful time and frequency domain analysis functions.

For example, the 100MHz clock signal shown in the figure below has obvious overshoot and duty cycle distortion in the time domain. From the measurement parameters, the rising and falling edges are not symmetrical.

In order to analyze the frequency domain impact that may be caused by the signal distortion, we can use the FFT function of the oscilloscope to perform spectrum analysis on the signal. As shown in the figure below, in the FFT function settings, you can set the center frequency (Center Frequency), spectrum width (Span), reference level (Reference Level), vertical scale (Scale), FFT analysis window type (Window), etc. You can also turn on the peak mark (Peak Annotation) function to mark the peak point that exceeds a certain power level. One thing to note is the setting of the resolution bandwidth (RBW). Since the resolution bandwidth in the frequency domain is inversely proportional to the acquisition time in the time domain, in some cases, if you need to reduce the resolution bandwidth to see the details of the spectrum, you need to adjust the time base scale in the time domain to display a longer waveform for FFT transformation.

After turning on the function, you can get the display result as shown in the figure below. The upper part is the original time domain waveform of the signal, and the lower part is the signal spectrum after FFT transformation. It can be seen that the signal has large energy components at the 2nd, 3rd, and 4th harmonics.

Furthermore, as shown in the figure below, we can also use the second mathematical function f2 to filter the original waveform of the signal, and then use the third mathematical function f3 to perform FFT transform on the waveform after the filter to obtain the signal spectrum after filtering.

The following figure shows the waveforms of various signals after the above mathematical processing. In addition to the original signal waveform coming from the real measured channel, we used the mathematical functions in 3 oscilloscopes and iterated the mathematical functions (for example, the third mathematical function is to perform FFT transformation on the waveform after low-pass filtering). Through the combination and iteration of this mathematical function, more complex waveform calculation and processing can be achieved.