Perfect timing: performing clock division with jitter and phase-noise measurements
Howell Mitchell, Silicon Laboratories - October 20, 2011
When measuring ultra-low-jitter
devices and equipment,
engineers must constantly ask
whether the measurement values
are from the DUT (device under
test) or from the test equipment.
Engineers are also always looking for methods of
expanding the reach of the equipment at hand. This
article describes some practical ways to handle situations
in which clock signals have been divided down
from higher-frequency VCOs (voltage-controlled
oscillators).Most modern equipment that measures jitter can be placed into one of two broad categories: time domain or frequency domain. Time-domain equipment typically comes in the form of a high-speed digital oscilloscope with high single-shot sampling bandwidth. Frequency-domain equipment usually comes in the form of a spectrum analyzer, a spectrum analyzer with phase-noise measurement capability, or a phase-noise analyzer. Each of these two categories of equipment has its own set of advantages and disadvantages. However, both measure the same phenomena, albeit with different approaches.
Peak cycle-to-cycle jitter is the
maximum difference between consecutive,
adjacent clock periods over a fixed
number of cycles—typically, 1000 or
10,000. It is used whenever there is a
need to limit the size of a sudden jump
in frequency. For example, when driving
a PLL (phase-locked loop), you may
want to limit the size of an instantaneous
change in frequency to ensure
that downstream PLLs remain in lock
(Reference 1 and Figure 1).
The peak-to-peak period jitter is the
difference between the largest clock
period and the smallest clock period
for all clock periods within an observation
window—again, typically 1000 or
10,000 cycles (Figure 2). It is a useful
specification for guaranteeing the setup-and-hold time of flip-flops in digital
systems. “Peak-to-peak” refers to the
difference between the smallest and the
largest sampled period value during a
measurement.
TIE (time-interval-error) jitter, or accumulated jitter—also known as phase jitter—is the actual deviation from the ideal clock period over all clock periods (Figure 3). It includes jitter at all jitter-modulation frequencies and commonly finds use in WAN (wide-area-network) timing applications, such as SONET (synchronous optical network), synchronous Ethernet, and OTN (optical-transport networking).
You can create various types of statistics,
such as rms (root-mean-square),
peak-to-peak, cycle-to-cycle, period,
and TIE jitter, although some are more
commonly used than others. Whenever
peak-to-peak statistics are used, the
number of samples taken must be large
enough to produce confidence in the
measurement. Such measurements typically
include 1000 to 10,000 samples.Time-domain equipment can directly measure peak-to-peak, cycle-to-cycle, period, and TIE jitter. This measurement approach permits the measurement of jitter for low-frequency clock, or carrier, signals. By employing FFTs (fast Fourier transforms), digital filters, and other techniques to postprocess the data, you can integrate the phase-noise value over a band of frequencies to generate rms-phase-jitter values. Time-domain equipment is better than frequency-domain systems at measuring datadependent jitter, making it useful for high-speed serial links that use SERDES (serializer/deserializer) technology.
Frequency-domain equipment cannot
directly measure peak-to-peak,
cycle-to-cycle, or period jitter; its native
ability is to measure the rms power of
a signal in a given frequency band.
Frequency-domain equipment is also
awkward for measuring data-dependent
jitter. However, high-quality frequency-domain
instruments have lower noise
floors than their time-domain counterparts, making frequency-domain units the instruments of
choice for ultra-low-phase-noise clock-signal measurements
that are free of data-dependent jitter (Table 1).Because this article focuses on the measurement of low-jitter
clock signals, it omits further discussion of time-domain
equipment except to mention that you can use various mathematical-estimation and -translation approaches to go from
one type of jitter measurement to another.
For example, it is
possible to use a crest factor and a desired BER (bit-error rate)
to go back and forth between peak-to-peak and rms jitter. Another example is using an FFT of time-domain data to provide
frequency-domain information and filtering. However,
most of these techniques rely on mathematical models, which
may be good approximations in most situations but have limitations.
As such, you should use them only carefully.
For example, it is
possible to use a crest factor and a desired BER (bit-error rate)
to go back and forth between peak-to-peak and rms jitter. Another example is using an FFT of time-domain data to provide
frequency-domain information and filtering. However,
most of these techniques rely on mathematical models, which
may be good approximations in most situations but have limitations.
As such, you should use them only carefully.
One issue that bears further investigation is the effect of
clock, or carrier, frequency on the jitter measurement. It is
intuitive that a clock signal divided down by an ideal divider
will have the same clock edge jitter at both its input and
output (Figure 4). In the figure, the top jittered signal with
frequency FO is divided by two using a perfect divider to produce
a clock frequency of FO/2. Both clock signals have the
same jitter, JO. Note that the jitter energy of the lower clock
signal is half that of the higher clock signal because there are
half as many edges in a given interval of time.The intuition that JO is the same for the two clock signals is for the most part true, despite the fact that the phase noise of a clock signal that is divided by two will be 6 dB lower than the phase noise of the original clock signal. Note that, for division by two, 6 dB=20×log2 (Reference 2).

The six curves in the figure are
essentially the same but with a vertical
separation of 6 dBc/Hz. The 6-dB
separation is relatively constant over
all offset frequencies and divisor values,
with one or two exceptions. On the
right side of the plot, in which the offset
from the clock, or carrier, is at its largest,
the relative vertical offsets between the
curves are compressed. The compression
increases as the clock frequency
decreases. This compression becomes
more pronounced as the clock frequency
and the phase-noise-curve values
decrease. The compression occurs
because the noise floor of the Agilent
Technologies model E5052B signal-source
analyzer nears the value of the
phase noise, or jitter generation, of the
Si5324 IC. The noise floor is an issue
only because of the combined effect of
the ultra-low jitter of the Si5324 and
the low carrier frequency. Table 2 lists
the corresponding jitter values for each
of the six plots with the jitter integrated
from 100 Hz to 20 MHz and all jitter
values in femtoseconds rms.
Notice that the jitter increases
slightly as the output frequency decreases—evidence that the output jitter is
relatively constant despite the fact that
6 dB separates the phase-noise curves
from one another. The rate at which
the jitter increases becomes more pronounced
at the lowest output frequencies.
Let’s examine the two sources of
the increased rms-phase-noise values:
instrument noise floor and aliasing.Noise floor, phase noise
The instrument’s noise floor can become the limiting factor of a measurement of phase noise of very-low-jitter clocks at low clock frequencies. At some point, you are measuring your equipment, not the DUT. Even though the phase-noise curves become monotonically smaller as the clock frequency decreases, the rms edge jitter remains almost constant because the phase-noise integration uses the clock period to scale the rms-jitter values.
To illustrate how this scenario
occurs, consider the process of phase-noise
integration to produce an rms-jitter
value. Most modern phase-noise
equipment produces a file that has two
columns—typically, a CSV (comma-separated-value) file. One of the two
columns lists the frequency offset from
the clock, or carrier, frequency in hertz.
The other column lists the phase-noise
values at that offset frequency in decibels
referenced to the carrier per hertz.
Thus, the columns contain data-point
pairs that describe the phase noise at a
given offset from the clock frequency.
Integration involves summing the area
under the curve for all of the frequency-offset points after converting the decibels-referenced-to-the-carrier-per-hertz values to linear
values using the following equation: Linear values=10(dBc/Hz)/10.
The equation for the area of a trapezoid in Figure 7 is used
to find the area described by two adjacent data-point pairs.
You find the area under the curve by summing the area of all
of the trapezoids. The final rms-jitter value is determined by
scaling the result by two factors: The value The other scaling factor converts the area sum so that it is no longer in UIs (unit intervals) but in units of time. This factor keeps the rms edge jitter’s values relatively constant, whereas the phase-noise values change. The equation for rms jitter is expressed as follows:

Aliasing
Aliasing is another cause of increasing
rms-jitter values with decreasing
clock frequency. For every division of
two, the upper half of a phase-noise plot
aliases down into the new lower-clock-frequency
phase plot. Because phase
noise is usually higher close to the clock
or carrier frequency and drops off as the
offset from the clock frequency increases,
relatively little phase noise aliases
down. However, when dividing by large
numbers, the effect becomes cumulative
and significant. For example, the difference
between the 1280- and the 640-MHz curves in Figure 1 is a constant
6 dB across the entire plot. As a result,
you would expect that the increased
rms-jitter values for the two curves in
Table 2 are due entirely to aliasing and
not to the instrument’s noise floor.


The spectra and phase-noise plots in Figures 9 through 13 illustrate aliasing. The signals in these examples use AM to illustrate aliasing and are not what would be expected in a typical application. Figures 9 and 10 show the spectra and phase-noise plots for the 3-GHz signal. The plots show symmetrical spurs at frequencies of 400 MHz above and below the 3-GHz clock frequency. When the spectrum shows two equal sidebands, the phase-noise plot of the same signal combines their effect into one spur 400 MHz from the 3-GHz carrier. A divide-by-four circuit then divides down the 3-GHz signal to produce 750 MHz.


Figures 11 and 12 show the spectra and phase-noise plots of the 750-MHz signal from the divide-by-four circuit. As a result of the division by four, the spurs at 2.6 and 3.4 GHz alias down to a sideband spur at 350 MHz. Note that 350 MHz is a frequency value that is the same 400 MHz from the 750-MHz carrier as 2.6 GHz is from the 3-GHz carrier. To further illustrate the aliasing, the 750-MHz signal is again divided down to 375 MHz. The 25-MHz spur in Figure 13 is an alias of the spur at 350 MHz in Figure 11; that is, 25 MHz=375 MHz−350 MHz.

As you can see, the instrumentation’s noise floor can become a limiting factor when measuring low-jitter clocks with low-frequency values. When a higher-frequency clock divides down the clock being measured, you can lower the value of the divider so that the measurement takes place at a higher frequency. However, this commonly used technique removes the jitter contribution from higher-frequency jitter components that would have been aliased down by the division. Although the resulting rms-jitter values may be artificially lower, this approach is acceptable in applications in which the phase noise that is far off of the corner is relatively small. When measuring low-frequency clocks with appropriately high amounts of jitter, use time-domain equipment because the measurement can be made at the actual desired output frequency, no matter how low the clock frequency may be.
Acknowledgment
This article originally appeared on EDN’s sister site, Test & Measurement Designline.
Author's biography
Howell Mitchell is a staff
applications engineer at Silicon
Laboratories, where he
is responsible for product
support for a family of jitter-attenuating PLL-based
timing devices. Before joining Silicon
Labs, he was a principal engineer at
MagiQ Technologies, where he worked in
quantum-key distribution. Mitchell holds
five patents in phase modulation and
quantum-key distribution. He received a
bachelor’s degree in physics from Bucknell
University (Lewisburg, PA) and a master’s
degree in engineering from Northeastern
University (Boston).Maxim Integrated 30th anniversary
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