How to use the FFT on an oscilloscope to its full potential

First, with a digital oscilloscope, our processing of waveforms is no longer simple. It is no longer just about looking at the waveform shape or measuring a few parameters.

We always want to do more processing on the collected data, and the oscilloscope has a more accurate understanding. It is more like a waveform analyzer. It is the dissatisfaction of engineers that gives us the motivation to constantly pursue the limit, because we often underestimate our potential. Where is the limit? Who was the first to use FFT (Fast Fourier Transform) in a digital oscilloscope? There are many opinions. It seems that suddenly, everyone has found that there is an FFT function on the oscilloscope, and it is all standard configuration. Although there is this function, the results are very different, and the speed and indicators are also different. The initial stage of anything is the same. First pursue it, then talk about differentiation. Moreover, the oscilloscope itself is a qualitative tool. Who cares about the accuracy of the oscilloscope's indicators in the frequency domain, except our lovely R&D engineers. The situation is changing. Many times, users hope to solve all problems with one instrument, because to be honest, many engineers do not have the conditions to put potentiometers, spectrum analyzers, oscilloscopes, and vector networks on their desks. In most cases, the oscilloscope performs software FFT operations on the collected time domain data samples to convert them into frequency domain samples, and then displays the frequency domain samples through data reorganization.

The capability of FFT depends on the following indicators: memory size, software operation speed, dynamic effective bit ENOB, and noise floor, because these indicators directly determine the refresh speed, dynamic range, sensitivity, and resolution bandwidth RBW after FFT.

Second, what problems can the oscilloscope's FFT solve?

Limited by the tools at hand (all engineers dream of having the most advanced oscilloscope and spectrum analyzer on their desks), and often engineers need to make qualitative observations before debugging circuits, so FFT becomes a good tool for viewing the spectrum. To be honest, many manufacturers' FFT functions are not satisfactory. There are two reasons. One is that they do not have the ability to do it well. To do spectrum analysis well still requires a lot of DSP experts and RF technical strength; the other is that they can do it well, but subjectively do not want to make FFT too powerful or too good, then how can I sell my spectrum analyzer? There is an opportunity cost issue here. However, FFT can still solve some problems, such as looking at the spectral range, harmonic components, harmonic proportion, roughly looking at the spectrum interference, etc., but it often brings some embarrassing problems. For example, when the sampling chip is composed of multiple pieces stacked together, the stacked spectrum lines will be exposed, the processing speed is so slow that it will make people frustrated, the background noise is a bit too outrageous, and the jitter component proportion is a bit messy. Of course, some good methods can be thought of to avoid these problems, such as limiting the FFT analysis samples so that it will not freeze during long-term storage of FFT, such as waveform averaging to reduce the background noise, etc.

3. Is the FFT of the oscilloscope useless?

It must be said that sometimes it is really useless. The processing speed is too slow, and the slightly larger sample is almost like a freeze. The RBW is too outrageous, the harmonic suppression ratio is very poor, the noise often drowns the harmonics, and the dynamic range is also very poor. But in fact, in many occasions, if the FFT function is good enough, it is not useless, but chicken legs. For example, testing the impulse response (characteristic curve) of filters and systems, distinguishing and locating noise interference sources, determining spurious radiation, jitter analysis, harmonic power analysis, EMI analysis. From this perspective, FFT has a lot of uses.

Fourth, we have maximized the spectrum analysis function on the oscilloscope. How did we do it?

First, we need to increase the speed of spectrum analysis and refresh it in real time, so you no longer have to endure the oscilloscope freezing when performing FFT conversion. Secondly, we have increased the RBW to 1Hz, which is almost only possible with spectrum analyzers. Our interface design is exactly the same as the operation of the spectrum analyzer, including center frequency, spectrum range, start spectrum, cutoff frequency, RBW setting, and window function setting. Almost all the settings of the spectrum analyzer have been transplanted. The following four aspects demonstrate how we have made the FFT function the best:

1. Dedicated digital down converter DDC

The traditional method is that the oscilloscope collects the signal samples and then uses the software algorithm to perform software calculations, which is very slow. Our method uses a dedicated hardware acceleration integrated circuit (ASIC) to hand over the FFT function to this hardware circuit to achieve a speed that is so fast that it hardly affects the refresh rate of the original waveform. Of course, this ASI requires a lot of money to develop. The core comparison uses a dedicated DDC circuit. Let's see how the traditional oscilloscope performs FFT.

Our oscilloscope fft principle

From the comparison of the above figures, we can see that a DDC process is performed before the window function. The user sets the center frequency, initial and cutoff frequencies. The result of the process is that only the frequency band of interest, or the set frequency band, is processed. The traditional method must perform FFT operations on all frequency bands, and then select a frequency to display. The amount of data calculated is very large. On the contrary, our principle is to only process the frequency band you are interested in or the initial frequency and cutoff frequency range you choose. Of course, in the extreme case, the full frequency band is also selected for processing. In this way, there is an opportunity to reduce the amount of processing and concentrate the processing power in the range after DDC. The following two figures more clearly tell the difference between the traditional method and our method. [page]

This approach brings two benefits:

a) Faster speed, frequency conversion to baseband processing will result in higher update rates and faster processing speed, saving processing time.
b) Better resolution bandwidth, because a better amplification factor will be used.

2. Use of hardware accelerator

In traditional solutions, software processing has always been used to implement functions such as statistical histograms, template test functions, and fft functions. In RS oscilloscopes, all functions are implemented using dedicated hardware circuits, freeing up the processor. Therefore, when performing histogram functions, template test functions, or fft functions that consume resources abnormally, the refresh rate is still very high, usually exceeding 60,000 times/s. This speed exceeds the refresh rate of all oscilloscopes on the market when no calculations are performed. This ensures that the refresh rate is still very high when performing complex waveform analysis, and the high refresh rate ensures the fast display rate of the real-time spectrum.

3. Application of overlapping FFT algorithm

The traditional oscilloscope FFT operation method is to collect a section, process a section, then collect again, and then process again.

Therefore, although data is collected and processed continuously, the spectrum of sporadic signals can be easily lost and cannot be detected.

RS oscilloscopes process the collected samples in segments, dividing the collected signal into many small segments for processing, so that the changes in the spectrum content in a single acquisition can be seen. However, optical segmentation processing cannot avoid loss, because before the FFT operation, there is already window function processing, and it is inevitable that spectrum information is lost between two adjacent frames. Therefore, we have adopted another more innovative method, using the FFT overlap algorithm, which greatly improves the influence of the window function and the loss of abnormal spectrum.

With the help of the display of simulated persistence, the display of real-time spectrum is more reliable and confident.

Summary of benefits:

a) It is helpful for detecting abnormal signals
b) It can display rare events that occur in a short period of time
c) It can improve the refresh rate of the spectrum (because before the FFT of one frame is completed, the FFT of a new frame has started)
d) It can distinguish multiple spectrum events in one FFT frame

4. Similar control interface and operation method to traditional spectrum analyzer

In the past, the way to operate an oscilloscope was to adjust the acquisition time to affect the resolution bandwidth, and then select the frequency band of interest for observation. Now, the method is to first select the center frequency, or select the start and end frequencies, and then adjust the spectrum observation method by directly adjusting the RBW, so that users who are used to spectrum analyzers can also get used to oscilloscopes.

There is another [page]

This table helps to understand which window function to use in which situation.

5. Use templates to achieve trigger settings in the frequency domain

Many people who are used to using oscilloscopes like the trigger function of oscilloscopes, and use various triggering methods to isolate various events, stabilize the display, and observe abnormalities. It is difficult to achieve triggering on traditional spectrum analyzers, but when we find the template triggering method of the oscilloscope, it is very easy to do it. The real-time spectrum of the time domain waveform is changed to the frequency domain for observation. With the help of some small tools of MASK test, it is easy to set and trigger. Because the shape of the template can be freely edited and the triggering actions can be freely combined, such waveform analysis has completely transcended the usage habits of the time domain and frequency domain, and completely integrated the thinking methods of the time domain and frequency domain for signals.

Red template area trigger instance

5. Development trend of spectrum analysis on oscilloscope

The analysis speed of oscilloscopes is getting faster and faster, the algorithms are becoming more scientific, and the storage depth is getting larger and larger. The FFT function is no longer dispensable as before. The ability of spectrum analysis depends on the FFT ability, the dynamic range, and the noise level. The spectrum analysis done by the principle of the oscilloscope needs to increase the dynamic range, which is nothing more than doing some time domain averaging before FFT to reduce noise, or increasing the storage depth, increasing RBW, and reducing asynchronous noise to achieve the purpose of increasing the dynamic range. In addition to doing the FFT function well, oscilloscope manufacturers must also have such a broad mind and regard technological integration and technological progress as opportunities. The driving force of innovation always brings new extremes. Whether we can defend a piece of territory depends on whether users buy it. Our goal is to continuously push the limits and continuously create new value for customers.