Measurement techniques developed during the past year make the task of troubleshooting power-supply designs both systematic and precise. Five techniques that incorporate WaveScan, a feature on LeCroy's WaveRunner Xi and WaveSurfer Xs oscilloscopes, can be used for rapidly characterizing and debugging switch-mode power-supply (SMPS) designs. This feature can be used to ensure the voltage transition edge rate and the frequency range of power-supply switching cycles. WaveScan also can be applied to detect nonmonotonic edges of the derivative of the turn-on voltage to monitor duty-cycle variation from the controller IC and to maintain the range of the control-loop response within the regulation time window.
These capabilities can be demonstrated using five real-world engineering challenges. In these examples, the results appear as images captured live from actual power circuits using two distinct modes for waveform scanning analysis. SMPS designers can immediately begin implementing these solutions with real-time oscilloscope tools using specialized techniques.
Switching-Cycle Frequency Stability
Identifying frequency stability problems within a SMPS can be greatly simplified using an automated method. When an oscilloscope detects the period of a waveform cycle, this value can be directly inverted to compute the instantaneous frequency of the specific waveform cycle. Using all-instance measurements, instead of considering only the value of one cycle within the acquired waveform, the frequency components of each cycle of an entire acquired input waveform can be computed as an array.
With a tolerance band applied to the instantaneous frequencies, the array of continuous instantaneous frequencies can be scanned by the oscilloscope to detect anomalous frequency content on a cycle-per-cycle basis. The waveform scanning technique incorporates this new approach and can report frequency anomalies graphically and numerically to identify all switching cycles that are outside the required frequency stability margins.
Shown in Fig. 1, a SMPS power transistor's drain-to-source voltage (VDS) is probed differentially on Channel 1. In this case, the threshold level of the frequency measurement parameter is set at 20% of the waveform amplitude level. This allows for the frequency-at-level measurement to include the switching frequency only, excluding any higher-frequency oscillations. The user should visually inspect the waveform to determine the appropriate threshold level setting.
For waveforms with oscillations within the period, the threshold level should be increased or decreased so that only rising edges of the primary switching cycle can become a triggering event. The scanning filter limit is set to the nominal instantaneous cycle frequency of 66.04 kHz, with a 50-Hz (or ± 25-Hz) delta defining the frequency tolerance band.
Triggering action of the resultant scan of frequency is set to save waveform data when the frequency stability margin is exceeded. Each complete waveform cycle that violates the defined scan tolerance band is displayed in an encompassing red box. Six cycles from the power supply have failed to meet these criteria in Fig. 1.
Power Device Turn-Off
The VDS of the switching MOSFET approaches zero during conduction and approaches the upper voltage rail during nonconduction. The voltage transition times correspond to device turn-on and turn-off transition periods. SMPS VDS transition can be determined from a single rise-time measurement for device turn-off and from a single fall-time measurement for device turn-on.
However, to ensure accurate device behavior, a single rise-time or fall-time measurement will not suffice. Statistically, a sample population of more than 1000 corresponds to a ±3-sigma measurement confidence, and a sample population of 10,000 corresponds to a ±3.5-sigma measurement confidence.
In Fig. 2, a ±3.5-sigma level of measurement confidence of VDS transition time has been obtained. Using all-instance measurements of rise time, the green iconic histogram (histicon) shows a statistically significant rise-time measurement population of more than 10,956 rise-time measurements.
Of the 10,956 measurements accumulated in the measurement histicon, waveform scanning has identified 1635 transitions that did not meet the user-defined measurement criteria for turn-off voltage transitions. All values that meet the scanning criteria have been logged into the red histogram. This technique also can be used to verify power-supply device turn-on compliance by substituting fall time in place of rise time in the scan criteria.
In this example, the five most recent rise-time anomalies, which correspond to the five red areas highlighted in the Channel 1 waveform trace, are listed in a tabular index in the upper-left edge of the display. Clicking on any of the index numbers will highlight that specific rise-time violation in bright yellow. In addition, the Z1 zoom trace corresponds to the selected rise-time anomaly. The red scan histogram contains the entire distribution of VDS transition failures as designated by user-defined criteria. Waveform data is automatically saved each time an anomaly is detected.
The histogram distribution can quickly identify modulation characteristics. Fig. 2 shows a Gaussian histogram distribution that contains one main mode with rise-time values clustered around the mean and randomly distributed out to the tails. This distribution shape indicates random noise on the rise-time measurement, which is due to the noise generated by any electrical circuit.
By contrast, the red histogram shown in Fig. 3 has two main modes in the histogram, a bimodal distribution. The rise-time measurements are clustered around two main modes and randomly distributed around the modes. In this case, there are a large number of rise-time measurements clustered around the (right-most) 105-ns rise-time measurement mode, and a more sparse distribution of rise-time measurements clustered around the minor (left-most) 90-ns mode.
Between the two clusters of rise-time measurements, virtually no rise-time measurement values fall in the range of 95 ns to 100 ns. The lack of data in this range indicates that a source of modulation such as crosstalk, coupling or oscillator effects are causing the rise-time value to modulate back and forth between the two main modes. The method of analyzing power-supply characteristics using scanning histograms provides insight that can help to rapidly identify the source of the problem in the SMPS.
Proper Gate Driving
To analyze the control-loop response of a power supply, the differential voltage between the gate and source of the power transistor is measured. The controller IC responds to load changes by varying the pulse widths. The gate-drive signal from the controller IC contains these varying pulse widths, which determine how long the device remains in the on state. The widths become narrower when the load is turned off.
Waveform scanning of duty cycle through the waveform will identify waveform pulses whose duty-cycle parametric values drop below a user-defined threshold. Use of waveform scanning for duty cycle is used to identify gate-drive failures that occur while the power supply has reached regulation.
By contrast, scanning for duty cycle during a load change can be used to identify key timing areas in which the MOSFET is reacting to reduce power-supply voltage at the output corresponding to the change in load. The duty-cycle values located in the scan can be either anomalies or identifiers for indicating proper operation timing, or both depending on the application context.
Scan overlay mode allows for duty cycles meeting user-defined criteria to be shown in a persistent display. The overlaid pulses need not occur from separate acquisitions. Adjacent pulses from within the same single waveform acquisition can be shown in an overlaid display, provided that they meet the user-defined criteria for scan identification. Fig. 4 shows an actual device in which a power-supply load changes from maximum to minimum load. All of the duty-cycle values below the threshold are highlighted in red, and the scan overlay shows a persistent mapping of all the narrow pulses corresponding to the user-defined criteria of duty cycles with values of less than 5%.
When monitoring events within time scales on the order of microseconds and shorter, scanning adjacent pulse cycles from the controller IC will show short-term fluctuations and anomalies in the pulse stream. When monitoring for longer intervals on the order of milliseconds, scanning the entire waveform array can reveal macro-effects occurring in the evolving set of duty-cycle measurements.
When applying a mathematical tracking function to a set of measurement parameter values, a new waveform showing a parameter value as a function of position is constructed. Unlike an acquired waveform, this new “Track” waveform has a measurement parameter value for its Y axis.
The X axis of the Track waveform is identical to the acquired pulse stream and shares the same X axis of time scaling. A track of the duty cycle will plot duty cycle as a function of time. As duty cycle changes slowly over time, the shape and timing characteristics of the track reveal important information not visually discernible from the input waveform itself. The overall shape can be used to characterize power-supply stability under load changes, line changes, soft-starts, dropouts, hot swaps and short circuits. In addition, the behavior of the control loop can be examined on a cycle-by-cycle basis.
In Fig. 5, the pink trace shows load-current transitioning from maximum to minimum load. The yellow waveform shows a detailed view of the gate-drive signal output from the controller IC in the turn-off region. The blue trace is a track of duty cycle, which shows a time-correlated view of duty-cycle values of the acquired voltage track. Measurement parameters can be applied to math functions to provide additional information.
By applying the width-measurement parameter to the track of duty cycle, the amount of time required for the controller IC pulses to reach regulation is determined. The width of the complete duty-cycle Track waveform can be examined to ensure that the control loop reaches regulation with a given time window. Beyond quantitative results, measurements on the track also help the user understand the control loop's startup response, view the plateau of gate duty cycles when the output voltage has not yet reached 5 V, and determine if regulation and steady-state were reached appropriately.
Detecting Nonmonotonic Edges
An important switching characteristic of a power supply's MOSFET is the linearity of its voltage turn-off transient. The rate of change of the switching transistor's output can be computed directly using the built-in capability of the oscilloscope's derivative math function. However, automatic detection of monotonicity of the dV/dt trace requires the oscilloscope to examine the edge of the derivative waveform using waveform scanning techniques.
In Fig. 6, nonmonotonic mode has been selected and applied to the derivative waveform of the input trace. The scope has detected a slight ring back in the dV/dt waveform and has highlighted this in red in the graticule. The dashed blue guidelines show the area where monotonicity is being monitored on each sweep. Waveform scanning has been configured to capture a screen image of each nonmonotonic edge detected during the scan.
Waveform scanning makes debugging a circuit fast and efficient. Automatic scans can monitor for pulses under steady load, drifts in frequency, widths that are too narrow, intermittent events and many other phenomena as shown within the five applications described previously.
When operating on a deep acquisition record with a large number of events, the tool quickly locates and identifies anomalies. When operating on multiple acquisitions, the tool continuously scans and executes actions based on user-defined criteria.
When a power-supply design challenge is described in terms of a timing measurement, then waveform scanning techniques rapidly find anomalous conditions. Measurement filtering methods are available to further narrow the search criteria.
The ability to view data values as histograms and overlaid measurements enables further analysis of the anomalies identified by the scan. These techniques can also be adapted for monitoring and troubleshooting virtually any power system to examine system behavior and identify anomalies as they occur. Furthermore, they can potentially be extended beyond design-phase troubleshooting into the realm of quality control during the production phase by monitoring waveform activity and reporting problems to the operator as they occur.