Dr Eric Bogatin Signal Integrity Evangelist Teledyne Le

Dr. Eric Bogatin, Signal Integrity Evangelist, Teledyne Le. Croy Dr. Alan Blankman, Product Manager, Teledyne Le. Croy

• 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) Rate • 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… USB 30: 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. 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: – 8 b/10 b, 64 b/66 b: “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!! 21 Early 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 = 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 Le. Croy Dr. Alan Blankman, Product Manager, Teledyne Le. Croy

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 = 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

Inter. Symbol Interference (ISI) Basics • Signature: Jitter on an edge function of bit history – TIE Track repeats for a repeating pattern (e. g. , PRBS 7) • 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: Jitter. Time 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” Correlated Jitter “Data Dependent Jitter” – “DDj” Duty Cycle Distortion “DCD” Intersymbol interference “ISI” “Random” – “Rj” Uncorrelated Jitter “BUJ” 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 Le. Croy Dr. Alan Blankman, Product Manager, Teledyne Le. Croy

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 PRBS 23 and certainly not PRBS 31 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 Rj. BUj 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 i. FFT 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” Eye. Width@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” Eye. Width@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=T 1) = 10 Prob(bit error, strobing at t=T 2) = 10 -7 Prob(bit error, strobing at t=T 3) = 10 Prob(bit error, strobing at t=T 4) =. . . BER 1 10 e-2 10 e-4 10 e-6 10 e-8 Eye width@BER = 10 -4 Tj (BER) = 1 – Eye Width Eye width@BER = 10 -8 10 e-10 10 e-12 Eye width@BER = 10 -12 10 e-14 T 1 T 2 T 3 T 4 10 e-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 Rj. BUj 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 Rj. BUj 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 log 10 BER 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 Tj 1 = α(BER 1) * Rj + Dj Tj 2 = α(BER 2) * Rj + Dj Use Tj values in vicinity of the BER that the user selects Tj 3 = α(BER 3) * Rj + Dj Tj 4 = α(BER 4) * 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 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 95

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 96

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 dual. Dirac jitter model. 97

Any Questions? 98

Thank You! • Always willing to answer more questions • Visit us at the Booth • Email us at: alan. blankman@teledynelecroy. com eric. bogatin@teledynelecroy. com 99