Transcript Document

Essentials of Jitter
Session 1
Intro to Jitter: What is Jitter, and
Why is it Important to Characterize?
Dr. Eric Bogatin, Signal Integrity Evangelist, Teledyne LeCroy
Dr. Alan Blankman, Product Manager, Teledyne LeCroy
• Note – this pptx file includes some custom
animation, and should be “presentation mode”
2
Jitter Matters. Yet it is Misunderstood.
Total Jitter - Tj
Deterministic Jitter - Dj
Random Jitter - Rj
Data Dependent Jitter - DDj
Intersymbol interference - ISI
Duty Cycle Distortion - DCD
Periodic Jitter - Pj
Bounded Uncorrelated Jitter - BUJ
Unbounded Jitter
Correlated Jitter
Uncorrelated Jitter
Dual-Dirac Model
Jitter PDF
Jitter CDF
Bathtub curve
Jitter Spectrum
Jitter Track
Jitter Histogram
Jitter Transfer Function
PLL Transfer Function
• Industry knowledge: Highly variable
– In practice, definition is also variable
• Misconceptions: Plenty
– E.g., Pk-pk vs. RMS vs. “Dual-Dirac”
• Results: Setup-dependent
– Scope specs & setup
– Data itself
– Jitter model, distribution, CDR, etc
• Our goal: Educate!
3
Jitter 101
• Three 45-minute sessions, with questions/breaks:
1 Introduction to Jitter:
What it is, and Why it Must be Measured
2 Jitter Components:
Getting to Know Different Jitter Components
3 Jitter Calculation in Action, and
How to Make Reliable Jitter Measurements
4
Why Measure Jitter?
The goal of your serial data design:
Transmit data without incurring (many) bit errors
… Analyzing jitter is important for achieving first-pass success
A typical channel consists
of multiple structures
and jitter sources
Source: PCI Express 2.0 Electrical Overview Presentation (w/revised text)
5
Fundamental Truth: Jitter  Bit Errors
• Timing jitter and noise cause edges to arrive
early/late compared to an expected arrival time
Wrong edge timing  Incorrect latching  Bit error
• Examples: When latching edge at time of vertical
Edge is too late!
cursor, bit = 0
Bit is low,
latched as “0”
Crossing
detection
level
Doesn’t cross
threshhold!
Latch (strobe) time
6
It’s all about Bit Error Rate/Ratio (BER)
• Specifications aim for extremely low BER
• The BER of a channel is important:
Not meeting BER requirements can be costly
– “Quality of Service” contractual requirements
– Datasheet specifications for clock and serdes Jitter
7
Planning for Jitter
• Electrical specs include budget & limits… USB30:
8
Definition of Timing Jitter
• Settle on this measurement-based definition:
Timing jitter is the result of an analysis of
Time Interval Error (TIE) measurements
– TIE results can be a histogrammed, tracked, FFT’d,
separated, decomposed, averaged, etc, etc, etc.
9
It all starts with TIE
Time Interval Error (TIE):
Measured Arrival Time of an edge
– Expected Arrival Time for the edge
Time Interval Error for the edge
Signal is late
• TIE describes how early or late an edge arrives
compared to its expected arrival time
• Multi-step process to make the measurement
10
Determination of Measured Arrival Times
• What gets done on a real-time digital oscilloscope:
– Determine the crossing times
Sample points
Interpolated
Edge arrival time
Next Step: find the expected arrival time
11
Determination of Expected Arrival Times
• Two scenarios relating to signaling methods:
1. Reference clock and/or strobe is transmitted
– e.g., DDR, clock/strobe signal latches bit
2. No clock signal is transmitted
– e.g., USB: clock and data recovery (CDR) circuit.
12
Determination of Expected Arrival Times
Scenario 1: When using a reference clock
• Expected arrival times of the data edges =
measured arrival times of the clock signal’s edges
Ref Clock
13
Determination of Expected Arrival Times
Scenario 2: Data only, no clock signal
• Use the data to determine the underlying clock
• Software Clock and Data Recovery (CDR)
algorithm finds bit rate
• Assumption: The correct bit rate minimizes
the average TIE of the entire waveform
14
Software CDR Steps
Step 1: Determine underlying bit rate
Step 2: Determine expected arrival times
15
Software CDR Algorithm
Step 1: Determine the Bit Rate
• Bit rate is determined via analysis of edge times
• Example algorithm:
– Histogram delta-T between successive rising edges
– Analyze to determine first-pass bit rate
Histogram of time between successive rising edges
2 UI
200 ps
4 UI
400 ps
6 UI
8 UI
600 ps
800 ps
– Create scatterplot of edge time vs. cumulative # UI’s
– Find slope of scatterplot 16
With ISI, it is a Bit Trickier
Clock is still recoverable via histogram
Histogram of time between successive rising edges
Clock is no longer recoverable
Equalization needed to open eye
17
Software CDR Challenges
• Algorithms break down when eye is closed
• CDRs are sensitive to long runs of 1 and 0 bits
– Signaling standards avoid this:
– 8b/10b, 64b/66b: “scrambling”
– Convert pattern to a different pattern with a more
desirable transition density
18
Software CDR Algorithm
Step 2: Determine Expected Arrival Times
• Method 1: Create a list using a constant UI time
– Nominal UI Time is 1/Bitrate
– i.e., assume that the underlying clock is “perfect”
• Method 2: Allow the UI time to vary using a
Phase-Locked Loop (PLL)
– Corrects for low-frequency jitter or “wander” in
underlying clock
• Oscilloscopes let you select from various PLL types
19
Software CDR Block Diagram
• The SW CDR allows the expected arrival times
to vary from the nominal interval of 1/Bitrate
• Oscilloscope emulates the PLL in a receiver
• Use a PLL that best matches your receiver
20
Finally, Determination of TIE Values
• Expected arrival times is essentially a clock
• Starting phase hasn’t been determined yet
Early!!
Late!!
Early
21
Late
Early
PLL & Jitter Transfer Functions
H
J
J=1-H
22
TIE Measurement Complete!
• Now have a list of TIE values, one for each edge
• This is the data set to be used for all jitter analysis
– Pk-Pk, sdev, histogram analysis
• Further analysis requires creation of a TIE Track
– Create a waveform out of the TIE measurements
– Shows how the TIE values change in the same
timebase as the source signal.
23
late
Interpolate
early
TIE value
Creating a TIE Track Waveform
• This gives our TIE measurements a timebase, and
facilitates further analysis
24
PLL tracks out LF Jitter, or “Wander”
• Here’s a waveform that has slowly varying jitter
• Goal of the PLL is to track out low-frequency jitter
• Let’s look at some TIE measurements & TIE Tracks
25
Simulation of Four Kinds of Jitter
Periodic Jitter
Intersymbol Interference
Random Jitter
Duty Cycle Distortion
26
Take-Away: Getting the most out of TIE
• Look at TIE measurements in different ways:
Statistical analysis
Pk-pk, SDEV, max, min
Histogram analysis
Are there multiple peaks? Skew?
Shape gives insight into jitter sources
Frequency analysis
Peaks & harmonics
Time-domain analysis
Lets you see the modulation scheme
Additional tools
Eye diagrams, Crosstalk Eye, ISI Plot, DDJ Plot & histogram
• Your scope may/may have the TIE meas standard
– Could depend on SW optioning
– TIE is performed “under the hood”
27
Is it “Correct” to use Pk-Pk?
• Peak-Peak is not a
well-defined statistic
– Random jitter is
unbounded
• Expected Peak-Peak
grows as you increase
population*
*measured pk-pk of a sample is random:
“unrepresentative” outliers happen
Expected pk-pk
28
Is it “Correct” to Use RMS?
• RMS isn’t meaningful if the jitter distribution isn’t
Gaussian
29
Session 1 Summary
• Measuring jitter is important
– Jitter causes bit errors, and bit errors are bad
• Specifications define jitter budget & methodology
• At its core, jitter is the variation in TIE
TIE = Measured Arrival time – Expected Arrival time
• Jitter distributions can be complicated, and not
easily summarized by RMS or pk-pk values
30
Any Questions?
31
Essentials of Jitter
Session 2
Introduction to Jitter Decomposition:
The Components of Jitter
Dr. Eric Bogatin, Signal Integrity Evangelist, Teledyne LeCroy
Dr. Alan Blankman, Product Manager, Teledyne LeCroy
Session 1 Quick Recap
• Measuring jitter is important
– Jitter causes bit errors, and bit errors are bad
• Specifications define jitter budget & methodology
• At its core, jitter is the variation in TIE
TIE = Measured Arrival time – Expected Arrival time
• Jitter distributions can be complicated, and not
easily summarized by RMS or pk-pk values
33
Session 2 Agenda
• Session 1 recap
• Clock signals vs. data signals: different signals,
different analysis requirements
• Types of jitter on NRZ serial data waveforms
– Descriptions and demonstrations
• Intro to the Dual-Dirac jitter model
34
Clock Jitter vs. Data Jitter
• Techniques used for each are often different
• Clock signals are…. Clocks:
– No data-dependent jitter, Gaussian jitter distribution
– Focus has been on measurement of period, “Pj” & “Rj”
• Data signals are complicated:
– Jitter depends on the data, not just on the channel
– Focus is on the side-effects of jitter: BER
35
Jitter Analysis of an NRZ Data Signal
36
The Components of Jitter
• Variety of jitter types and jitter aggressors
• Fairly large alphabet soup…
Total Jitter - Tj
Random Jitter - Rj
Deterministic Jitter - Dj
Data Dependent Jitter - DDj
Intersymbol Interference - ISI
Duty Cycle Distortion - DCD
(Un)Correlated Jitter
Periodic Jitter - Pj
(Un)Bounded Jitter
37
Simulation of Four Kinds of Jitter
Periodic Jitter
Intersymbol Interference
Random Jitter
Duty Cycle Distortion
38
InterSymbol Interference (ISI) Basics
• Signature: Jitter on an edge function of bit history
– TIE Track repeats for a repeating pattern (e.g., PRBS7)
• Bounded. And depends on data
• Causes:
– Reflections change the shape of an edge
– Limited channel bandwidth
– Know the single bit response of your channel
39
Further Diagnosing ISI
(described by S-parameter matrix)
200 psec UI
ISI
“echoes of bits past”
40
Simulation of ISI
41
Duty Cycle Distortion (DCD) Basics
• Signature: two states in TIE Track and histogram
• Measures difference in bit width for 0 and 1 bits
• Bounded.
• Caused by:
– Variation in crossing level and/or
shift in signal’s offset
42
Periodic Jitter Basics
• Signature: pks in spectrum, bowl-shape histogram
• Bounded.
• Called Pj; pure sinusoidal would be SJ
• Caused by:
– Coupling in of other periodic signals in system
– Power supply switching frequency
44
Understanding Random Jitter
• Signature: Gaussian tails on jitter histogram
• Unbounded…. Gaussian
• Causes:
– Thermal noise, shot noise, flicker noise
– Random variations in otherwise uniform structures
– Deterministic jitter contributors create an overall
46 tails.
distribution with Gaussian
Understanding Random Jitter
• Random jitter grows
without bound, and
eventually closes the eye
• Take-away:
Peak-to-peak is only
meaningful for a given
sample size
Expected pk-pk
47
Histogram Growth
Source: JitterTime Consulting
48
Simulation of Random Jitter
49
“Other” Jitter
• Uncorrelated to the pattern…
• Not Gaussian, not periodic… OBUJ
– Other Bounded Uncorrelated Jitter
• Crosstalk / interference
– Interference translates into jitter
• Potentially broadband; spectral noise floor rises:
w/XTALK
50
Sources of OBUJ
•
•
•
•
•
Crosstalk from non-repeating data (e.g., live traffic)
High rate frequency modulation on Pj component
Power supply switching noise
EMI radiation
Simultaneous-switching noise
51
Putting it together: Overall Jitter Distribution
52
Classification of Jitter Types
Total Jitter – “Tj”
Bounded
Unbounded
“Deterministic” – “Dj”
“Random” – “Rj”
Correlated Jitter
Uncorrelated Jitter
“Data Dependent Jitter” – “DDj”
“BUJ”
Duty Cycle
Distortion
“DCD”
Intersymbol
interference
“ISI”
Periodic
“Pj”
Other
“OBUJ”
• Problem:
– These names represent jitter types, but not the measurement methodology
– i.e, are they RMS, peak-peak, etc?
53
Jitter Analysis Goals
• Quantify each of these components
• For Total Jitter
– Want predictor of jitter for small bit error rates
– How do we find Tj for a huge data set (e.g., 1012 bits)?
– To do this, need to extrapolate the random jitter
– Cannot use peak-peak to characterize total jitter
• We want to separate Tj into Dj, Rj
– Need a model to guide this process
54
Defacto Industry Standard:
The Dual-Dirac Jitter Model
• Analysis using the dual-Dirac jitter model gives:
– Estimate of Tj at any BER
– “Rj” and “Dj” values
• The model describes jitter as:
– Deterministic jitter: the separation
between two Diracs
– Random jitter as a Gaussian
• Fits Tj, Rj and Dj to the equation
– Tj(BER) = α(BER) * Rj(δδ) + Dj(δδ)
55
Session 2 Summary
• Clock signals and data signals have different jitter
measurement reqs
Jitter Type
Classification
Random
Unbounded
• Explored jitter types
Periodic
Bounded
• Explored how each type Intersymbol interference
Bounded
Duty Cycle Distortion
Bounded
appears in an eye,
histo, track, spectrum Other bounded uncorrelated Bounded
• Jitter sources come together to form an overall
jitter distribution or PDF
• Bounded = Deterministic, Unbounded = Random
56
Any Questions?
57
Essentials of Jitter
Session 3
Jitter Calculation in Action
How to Make Reliable Measurements
Dr. Eric Bogatin, Signal Integrity Evangelist, Teledyne LeCroy
Dr. Alan Blankman, Product Manager, Teledyne LeCroy
Session 1 Quick Recap
• Measuring jitter is important
– Jitter causes bit errors, and bit errors are bad
• Specifications define jitter budget & meas method
• At its core, jitter is the quantification TIE meas
TIE = Meas. Arrival time – Expected Arrival time
• Jitter distributions can be complicated, and not
easily summarized by RMS or pk-pk values
59
Session 2 Quick Recap
• Clock signals and data signals have different jitter
measurement reqs
Type
Classification
Random
Unbounded
• Explored jitter types
Periodic
Bounded
• Explored how each type Intersymbol interference
Bounded
Duty Cycle Distortion
Bounded
appears in an eye,
histo, track, spectrum Other bounded uncorrelated Bounded
• Jitter sources come together to form an overall
jitter distribution
• Bounded = Deterministic, Unbounded = Random
60
Session 3 Agenda
Separating out the Components
• In the last session we looked at idealized jitter
distributions
• Algorithms to analyze jitter need to be
generalized, and to work on any type of signal
• In this session, we will discuss how this is done
Total Jitter - Tj
Deterministic Jitter - Dj
Random Jitter - Rj
Data Dependent Jitter - DDj
Intersymbol interference - ISI
Duty Cycle Distortion - DCD
Periodic Jitter - Pj
61
Jitter Calculation Flow
1. Make TIE measurements
2. Form TIE Track
3. Quantify data-dependent jitter
4. Remove DDJ from TIE Track to get Track of Rj & BUj
5. Quantify Pj from spectrum of Rj & BUj Track
6. Remove Pj from spectrum of Rj & BUj Track
7. Quantify sigma of Gaussian shape
8. Extrapolate tails
9. Bring DDJ back in to get extrapolated jitter PDF, CDF
10.Perform dual-Dirac fit to get Tj, Rj and Dj
62
Jitter Calculation Flow
1. Make TIE measurements
2. Form TIE Track
3. Quantify data-dependent jitter
4. Remove DDJ from TIE Track to get Track of Rj & BUj
5. Quantify Pj from spectrum of Rj & BUj Track
6. Remove Pj from spectrum of Rj & BUj Track
7. Quantify sigma of Gaussian shape
8. Extrapolate tails
9. Bring DDJ back in to get extrapolated jitter PDF, CDF
10.Perform dual-Dirac fit to get Tj, Rj and Dj
63
3. DDJ Determination
• Data-dependent jitter can be characterized via
pattern analysis of TIE Track
Wfm
TIE
Track
• The more iterations you acquire the better
• Without a repeating pattern, look for repetitions
of N-length bit sequences
64
Determining DDj
Averaged TIE Track
is “DDj Plot”
: Positive edges
: Negative edges
65
Sources of Error in DDj Measurement
• Too few iterations of the pattern creates problems
– Results in insufficient statistics for DDJ Plot
– Results in lower accuracy Rj + BUj track
– Results in lower accuracy extrapolation
– And so on…
• Trade off: shorter patterns are faster to acquire,
but do not fully “exercise” DDJ space
• Goal of dual-Dirac is to get fast result, but can’t do
this with PRBS23 and certainly not PRBS31
66
DDj for Extremely Long Patterns
• DDJ histogram now includes tails that are will look
like Rj to the receiver
• Result is that Rj(δδ) grows with pattern length
67
Jitter Calculation Flow
1. Make TIE measurements
2. Form TIE Track
3. Quantify data-dependent jitter
4. Remove DDJ from TIE Track to get Track of Rj & BUj
5. Quantify Pj from spectrum of Rj & BUj Track
6. Remove Pj from spectrum of Rj & BUj Track
7. Quantify sigma of Gaussian shape
8. Extrapolate tails
9. Bring DDJ back in to get extrapolated jitter PDF, CDF
10.Perform dual-Dirac fit to get Tj, Rj and Dj
68
Determine TIE Track w/o DDJ
• Subtract out the DDJ for each edge from the
overall TIE Track
• What’s left is a TIE Track that includes:
– Random jitter, Pj, OBUJ  call this RjBUj Track
• More amenable for Pj and Rj analysis
69
Jitter Calculation Flow
1. Make TIE measurements
2. Form TIE Track
3. Quantify data-dependent jitter
4. Remove DDJ from TIE Track to get Track of Rj & BUj
5. Quantify Pj from spectrum of Rj & BUj Track
6. Remove Pj from spectrum of Rj & BUj Track
7. Quantify sigma of Gaussian shape
8. Extrapolate tails
9. Bring DDJ back in to get extrapolated jitter PDF, CDF
10.Perform dual-Dirac fit to get Tj, Rj and Dj
70
Measuring Pj via Spectral Analysis
• Peaks in the spectrum are from periodic jitter
• Pk-pk of iFFT of spectral peaks becomes Pj
71
Finding Peaks
• Our eyes finds peaks quickly
• Doing this programmatically is a bit more
challenging
– Need to establish the floor over which the peaks sit
– The floor is certainly not level
72
Jitter Calculation Flow
1. Make TIE measurements
2. Form TIE Track
3. Quantify data-dependent jitter
4. Remove DDJ from TIE Track to get Track of Rj & BUj
5. Quantify Pj from spectrum of Rj & BUj Track
6. Remove Pj from spectrum of Rj & BUj Track
7. Quantify sigma of Gaussian shape
8. Extrapolate tails
9. Bring DDJ back in to get extrapolated jitter PDF, CDF
10.Perform dual-Dirac fit to get Tj, Rj and Dj
73
Determination of σ
• Need this to extrapolate the tails of the Gaussian
– Key to predicting Tj for BER beyond what’s measured
74
Jitter Calculation Flow
1. Make TIE measurements
2. Form TIE Track
3. Quantify data-dependent jitter
4. Remove DDJ from TIE Track to get Track of Rj & BUj
5. Quantify Pj from spectrum of Rj & BUj Track
6. Remove Pj from spectrum of Rj & BUj Track
7. Quantify sigma of Gaussian shape
8. Extrapolate tails
9. Bring DDJ back in to get extrapolated jitter PDF, CDF
10.Perform dual-Dirac fit to get Tj, Rj and Dj
75
Extrapolating the Random Jitter
• Why extrapolate?
– Want to estimate “Total Jitter” for a very large data set
• Acquired data set usually has ~105 to 107 events
– Standards extrapolate out to 1012 or higher
• Concept at play:
– Want to establish an (in)confidence interval
– Let’s understand this further…
76
Jitter as it Relates to # of Measurements
• Example: Eye diagram with 107 TIE measurements
107 UI (measured)
“Total Jitter”
EyeWidth@107
1 UI
Total Jitter + Eye Width = 1 UI
77
Jitter as it Relates to # of Measurements
• Now simulate an eye with 1012 TIE measurements
1012 UI (simulated)
“Total Jitter”
EyeWidth@1012
1 UI
Total Jitter + Eye Width = 1 UI
78
Jitter as it Relates to Bit Error Ratio
• What’s the probability of a bit error when
strobing the bit a particular place in the eye?
-1-5
Prob(bit
error,
strobing
at
t=T1)
=
10
Prob(bit error, strobing at t=T2) = 10 -7
Prob(bit error, strobing at t=T3) = 10
Prob(bit error, strobing at t=T4) = ...
BER
1
10e-2
10e-4
10e-6
10e-8
Eye width@BER = 10-4
Tj (BER)
= 1 – Eye Width
Eye width@BER = 10-8
10e-10
10e-12
Eye width@BER = 10-12
10e-14
T1 T2 T3 T4
10e-16
79
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
UI
1
What does Tj(BER) mean?
• Tj(BER) is a confidence interval
– Time interval with prob = 1/BER
in the tails of the distribution
Tj is a confidence interval
1/BER in tails
Tj@BER
• The BER of interest is often 10-12
• Measuring Tj(BER<10-7) requires extrapolation of
the observed jitter distribution
80
Introducing the Dual-Dirac Jitter Model
• Model that allows determination of Tj, Rj, Dj for
BER levels that cannot be acquired on a scope
• MJSQ, Fibre Channel in ~2004
• Analysis using the dual-Dirac jitter model returns:
– Estimate of Tj at any BER
– Estimates of “random jitter” and “deterministic jitter”
– Tj(BER) = α(BER) * Rj + Dj
– Tj, Rj, Dj are formally Tj(BER), Rj(δδ), Dj(δδ)
81
Determination of Tj/Rj/Dj using the
dual-Dirac model
• Dual-Dirac model :
– Two Dirac functions separated
by Dj(δδ)  Deterministic Jitter
– Gaussian  Random Jitter
– Convolve to form overall PDF
– Tj= α(BER) *Rj(δδ) + Dj(δδ)
• Mission:
– Find σ
– Extrapolate tails of random jitter
– Find μl and μ r
82
Jitter Calculation Flow
1. Make TIE measurements
2. Form TIE Track
3. Quantify data-dependent jitter
4. Remove DDJ from TIE Track to get Track of Rj & BUj
5. Quantify Pj from spectrum of Rj & BUj Track
6. Remove Pj from spectrum of Rj & BUj Track
7. Quantify sigma of Gaussian shape
8. Extrapolate tails
9. Bring DDJ back in to get extrapolated jitter PDF, CDF
10.Perform dual-Dirac fit to get Tj, Rj and Dj
83
Extrapolating the Random Jitter
• The RjBUj Histogram is extrapolated in “Q-Scale”
– Q-scale transform curvy Gaussian into triangle with
sides of slope = σ
84
Jitter Calculation Flow
1. Make TIE measurements
2. Form TIE Track
3. Quantify data-dependent jitter
4. Remove DDJ from TIE Track to get Track of Rj & BUj
5. Quantify Pj from spectrum of Rj & BUj Track
6. Remove Pj from spectrum of Rj & BUj Track
7. Quantify sigma of Gaussian shape
8. Extrapolate tails
9. Bring DDJ back in to get extrapolated jitter PDF, CDF
10.Perform dual-Dirac fit to get Tj, Rj and Dj
85
Determining the Extrapolated PDF/CDF
• Convolve extrapolated RjBUj with DDj histogram
• Yields overall extrapolated jitter histogram
• When appropriately normalized, this is the
jitter probability density function (PDF)
86
Dual-Dirac Determination of Tj/Rj/Dj
• Integrate PDF from left/right to the middle separately
• This yields the Cumulative Density Function (CDF)
– “Inside-out” bathtub curve
• Width of CDF is Tj(BER) for any BER level
log10BER
0
-2
-4
-6
-8
-10
-12
-14
0.2
0.3
0.4
0.5
87
0.6
0.7
0.8 0.9
Jitter Calculation Flow
1. Make TIE measurements
2. Form TIE Track
3. Quantify data-dependent jitter
4. Remove DDJ from TIE Track to get Track of Rj & BUj
5. Quantify Pj from spectrum of Rj & BUj Track
6. Remove Pj from spectrum of Rj & BUj Track
7. Quantify sigma of Gaussian shape
8. Extrapolate tails
9. Bring DDJ back in to get extrapolated jitter PDF, CDF
10.Perform dual-Dirac fit to get Tj, Rj and Dj
88
Last Step: Perform Fit
• Fit to Tj= α(BER) *Rj(δδ) + Dj(δδ) letting both
Rj(δδ) and Dj(δδ) vary
Tj1 = α(BER1) * Rj + Dj
Tj2 = α(BER2) * Rj + Dj
Use Tj values in vicinity
of the BER that the user selects
Tj3 = α(BER3) * Rj + Dj
Tj4 = α(BER4) * Rj + Dj
α(BER):
89
Strengths/Weaknesses of Dual-Dirac
• Everyone is using it
• Total jitter has a well-defined meaning
– Tj’s can be compared
• It’s a model, and models aren’t perfect
– Deterministic jitter is never two diracs, except for DCD
• Major side-effect of model: Dj(δδ) < Dj(pk-pk)
90
Dj(δδ)
Dj(pk-pk)
91
How to Get the Best Results
• Some “Jitter Calculation Models” give lower
results for Rj, or for Dj, or for Tj…
• Results are jitter model-dependent
• Results are dependent on certain scope settings
• Leads to the question: What answer is correct?
92
Scope Properties that Affect Results
Scope bandwidth
Scope noise
Affects overall amount of noise that will be digitized, and which
Affects the determination of the edge timing
Sample clock accuracy
Affects the overall accuracy of timing measurements.
Sample clock jitter
Affects the accuracy of the timing for each individual sample
point
Channel-channel jitter
Affects timing measurements between channels.
93
Signal Properties that Affect Results
Pattern Length
Pattern type
Affects ISI and DDj, affects the # of repetitions acquired for pattern
averaging
DDj tails can look like Rj
Signal Noise
Signal Slew rate
Affects the jitter noise floor, which proportional to noise/slew rate
Bitrate
Bitrate affects amount of DDj
Amplitude
In conjunction with scope setup, need to ensure appropriate gain
setting for signal amplitude.
94
Scope Settings that Affect Results
Oscilloscope Sample Rate
Affects overall amount of noise that will be digitized, and
which affects edge timing
Deskew of differential inputs
Affects slew rate of calculated differential signal, which
affects the conversion of noise to jitter
Acquisition length
Determines how many iterations of the pattern are
measured
Vertical gain setting
Scope noise varies with the gain range
In conjunction with signal level, need to ensure
appropriate gain setting for signal amplitude
Jitter model
Different model  different results
PLL type
Affects the amount of low frequency jitter that is tracked
and not quantified as joi
Crossing level
Affects relative timing of edges
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Industry Trends
• Increasing bit rate  tighter jitter budgets
• At the bleeding edge, femtoseconds matter
– Rj numbers like 150 fs are no longer effectively 0
– PCIe 2.0 data : Rj 4.0 ps RMS
• Challenge to instrument manufacturers to
continue to lower the noise floor
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Session 3 Summary
• TIE is the fundamental nugget of jitter
• Determination of DDj, ISI, DCD, Pj: can be
measured with acquired TIE measurements
• DDJ and ISI are best measured with sufficient
statistics with a suitable length pattern
• Tj is defined as Tj(BER) rather than Tj(pk-pk)
• Dj(dd), Rj(dd) are the result of a fit to the dualDirac jitter model.
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Any Questions?
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Thank You!
• Always willing to answer more questions
• Visit us at the Booth
• Email us at:
[email protected]
[email protected]
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