Learn how oscilloscope histograms visualize measurement variation, identify distribution shapes, and quantify rare events in waveform timing and amplitude data.
Instrumented microcontroller boards and active voltage-rail probes make it possible to directly observe on-die power rail behavior, including sag, noise, and ground bounce.
A primary engineering task for 48 Volt power conversion systems using bridge topologies is to ensure adequate dead time to prevent catastrophic shoot through occurring when both HI and LO FETs conduct at the same time. Being able to accurately measure dead time is therefore of critical importance. Part 1 of this series dealt with the basic static dead time measurement. Part 2 will deal with studying the dynamic changes in the dead time measurement using statistics, tracks and histograms.
As we learned in part 1, the dead time delay is measured using two instances of the measurement parameter Delta Time at Level (dt@lvl) as shown in Figure 1.
Figure 1: Setup to measure dead time using an automated measurement parameter
In the Figure 1, the dtime@level parameters is automating the measurement of dead times in a single acquisition with thousands of half-bridge switching transitions.
Two different measurement parameters were used to measure the two different rising/falling edge pair transitions. Only a single value is shown in Figure 2 (all values are measured, just not displayed in this instance), and the value shown is last value in the full acquisition. You should ask the question: how do the measurements vary with time?
Figure 2: Dead-time measurement result of setups made as shown in Figure 1
To find the answer, turn on the measurement parameter statistics, as shown in Figure 3.
Figure 3: Additional statistical data on the dead-time measurements as shown in Figure 2
P2 and P3 have each collected 10,000 values of dead-times during the 10 ms acquisition, and the statistical table shows the last value, mean, minimum, maximum, standard deviation and number of values reported. The minimum values of both parameters are positive, indicating that shoot though has not occurred. However, this presents is very incomplete picture of the dead-time variation over time – for that we would use a Track function, or a plot of measurement values over time, time synchronized and time correlated to the original acquisition.
Figure 4 shows the track of a frequency measurement taken from another test, for purpose of illustration. Frequency decreases from ~1 MHz to ~956 kHz in eight steps corresponding to the cycles of the source waveform, which are clearly visible on the track.
Figure 4: Track function (F1) displaying periodic change in measurement per period Frequency value, plotted time-correlated to the measured source waveform (C1). The Track function shows that there is a continuously decreasing frequency of the acquired sinewave.
Figure 5 shows the same technique – a Track function – used to display the the two dt@lvl (dead time) parameter measurements varying in time, shown in Figure 5 (orange trace and red trace).
The track view confirms graphically that there are no delay values that are negative. Each value in a track matches up with its source on the VgsLo or VgsHi waveform. This allows any anomalous events seen on the track to be matched to the source for analysis.
Figure 5: Track functions (in orange and red) showing variation of measured dead-time values over time, time synchronized with the acquired waveforms.
Tracks can be measured just with cursors, measurement parameters, and other tools just like any other waveform. Histograms can also be used to view the distribution of the statistical data set.
For more information watch the webinar Best Practices for 48 V Power Conversion Testing. Click the link above to view the series and register for this particular webinar, or use the button below to do the same. You can also preview the webinar content by downloading the webinar transcript file.
Kenneth Johnson has managed oscilloscope and probe product lines, power applications and HV T&M equipment. He holds a B.S.E.E. from Rensselaer Polytechnic Institute. He enjoys playing ice hockey, cross-country skiing and bicycling in his spare time.
