Introduction
Modern oscilloscopes contain many tools that can be used for analyzing data, including Track and Trend math functions. Both Tracks and Trends graphically display measurement results and locate anomalies. The main similarity between Tracks and Trends is that the Y-axis of both operators is the measurement parameter itself (for example, Pulse Width, Duty Cycle, Rise Time, Slew Rate, etc.). The main difference between the two math operators is their X-axis, in which the Track uses the identical X-axis and synchronous horizontal scaling as the input waveform, whereas the Trend uses units of chronology.
Tracks
The Track provides valuable debugging information by directly pointing to an area of interest. Notice the negative-going spike in the Track waveform in Figure 1.
Figure 1 occurs at the point in time where the input waveform reaches its most narrow pulse width, and the Track instantly finds it, indicating when one measurement deviates from the others in the graph. The Track identifies the exact location in time where the narrowest or widest pulse width has occurred, and fully describes the measurement changes occurring throughout the entire waveform. Since oscilloscopes can acquire thousands or even millions of waveform edges within a single acquisition, the Track allows an engineer to quickly "find the needle in a haystack".
In addition to locating anomalies, Tracks can synchronously demodulate a waveform, which is useful for uncovering repeating characteristics within signals. When a repeating pattern occurs, the pattern is often not visible to the user because changes in the waveform are quite subtle relative to the repetition time. Consider the case of the same repeating pulse width modulation pattern. Shown in Figure 2, the Track operator has demodulated a PWM waveform using a much longer time interval. The shape of the pulse width modulation reveals a repeating modulation pattern which would not have been obvious to an engineer if not for the shape of the Track, because at the time scale needed to capture several repetitions of the PWM pattern, there are at least hundreds of pulse widths on the screen. This allows for the user to identify which underlying data patterns exists within the data and their rate of occurrence.
In order to determine how many pulse widths comprise one full repetition of a PWM pattern, the user can measure the frequency of the input waveform and the frequency of the pattern itself, and calculate the ratio of the two frequencies, as shown in Figure 2. By graphing primary measurements, then demodulating the pulse train with the Track operator, secondary measurements on the Track provide a complete description of the underlying pulse width modulation scheme. This type of application is where the Track math operator is at its best, allowing an engineer to combine math and measurement operators in a simple way to learn a great deal about a circuit's behavior.
Trends
To illustrate an important distinction between Tracks and Trends, the Trend math operator in Figure 3 is now applied to the same signal without reacquiring the input waveform.
Note that unlike a Track, the Trend is not time-synchronized to the input waveform. Only the order of events, and not the timing of events, is retained. The underlying shape of the Track may be displayed in the Trend because the same pulse width measurement values from a single acquisition are displayed in the same sequence—however, the timing information of when each of the pulses has occurred is not retained in the Trend. Therefore, unlike the Track, the Trend does not point to the location of an anomaly. Without time scaling, the Trend does not have frequency information needed to demodulate an input waveform.
Unlike the Track, the Trend can retain measurement results from previous acquisitions, which helps an engineer to retain a history of previous measurements compared with new measurements. The Trend allows for an engineer to observe long term changes due to timing drift across multiple acquisitions, which would not be visible in a Track. For example, when heating or cooling the device under test in a thermal chamber to test environmental effects, the Trend can show long-term variation in measurement values as the device temperature changes in the thermal chamber.
Trends are ideal for data logging, especially when characterizing slowly changing phenomena. Figure 4 shows only a single pulse within the waveform record, but there are many values in the Trend, because multiple waveforms are acquired, although only a single pulse is captured each time. The acquired pulse in Figure 5 is wider than the acquired pulse in Figure 4, and therefore retains the full record of previous pulse widths and appends the new wider pulse value to the existing Trend, showing the history and evolving changes in the measurement.
Tracks, on the other hand, are not ideal when there is a very low number of measurements per waveform. Shown in Figure 6, a Track and Trend are applied simultaneously to a single pulse. Because the Trend retains the history of pulses measured during previous acquisitions, it is optimal for this type of application. By contrast, the Track operator is synchronized to the same time scale as the acquired pulse and displays a flat line corresponding to the single pulse width which is overwritten with each subsequent acquisition.
Note that this discussion focused only on the Track and Trend of the Pulse Width measurement in order to bring focus to the contrasts between the two math operators, but Tracks and Trends can graph hundreds of other types of input measurement values including Rise Time, Fall Time, Duty Cycle, Skew, Slew Rate, Setup and Hold, custom scripted values, etc., which allows an engineer to characterize a nearly limitless set of varying circuit behavior.
Conclusion
In summary, both Track and Trend operators provide graphs of measurement results and identify anomalies. Track operators incorporate the same horizontal scale as the source waveform measured, allowing the ability to demodulate a waveform and to pinpoint the exact locations of an anomaly, which is extremely valuable for correlation and cause-and-effect analysis. Trend operators by contrast are not synchronized to the input waveform, allowing the advantages of a reduced record size and the ability to retain the history of measurements that span across multiple acquisitions. Depending on the application needs, either of these two operators can be the optimal choice.
