Matlab Model Serdes Systems
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This skill provides a comprehensive framework for designing and validating Serializer/Deserializer (SerDes) systems. It covers the full development lifecycle, from initial architecture exploration and channel characterization to parameter optimization and the generation of industry-standard IBIS-AMI models. Users can simulate NRZ and PAM-N links, evaluate equalization architectures like FFE, CTLE, and DFE, and perform compliance testing using statistical and time-domain analysis.
What it does
- Models and simulates high-speed serial and parallel links including NRZ and PAM-N signaling.
- Characterizes channels using loss models, S-parameter files, and crosstalk scenarios.
- Optimizes equalization architectures such as FFE, CTLE, and DFE using genetic algorithms.
- Performs statistical and time-domain analysis including eye diagrams and jitter decomposition.
- Exports designs to Simulink for IBIS-AMI model generation and compilation.
- Validates compiled models against behavioral baselines for compliance and performance.
When to use it
- When designing SerDes links for specific data rates and channel loss profiles.
- When evaluating and tuning equalization architectures to meet signal integrity targets.
- When you need to generate IBIS-AMI models for Tx, Rx, Redriver, or Retimer components.
- When performing compliance checks using eye masks and jitter budgets.
- When analyzing captured waveforms from oscilloscopes or simulations.
Advantages
- Full-arc workflow from design exploration to compiled model delivery.
- Supports advanced PAM-N modulation schemes up to PAM16.
- Enables programmatic parameter sweeps and optimization for design space exploration.
- Provides robust validation tools to ensure compiled models match behavioral references.
- Integrates seamlessly with Simulink for complex time-domain system simulations.
Usage examples
- “Create a SerdesSystem object for a PAM4 link and run a statistical analysis.”
- The agent initializes a SerdesSystem with specified Tx/Rx blocks, configures the channel, and executes the analysis() method to generate eye diagrams and performance metrics.
- “Export my current SerDes design to a Simulink model for AMI generation.”
- The agent uses exportToSimulink(sys) to generate the Simulink representation and prepares the IbisAmiManager settings for model compilation.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/matlab/matlab-model-serdes-systems/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip matlab-model-serdes-systems.zip -d .agents/skills
Documentation by MathWorks
Modeling and Simulating SerDes Systems
Design, analyze, and deliver high-speed serial link models using SerDes Toolbox. From architecture exploration through IBIS-AMI model generation, covers the full workflow for NRZ and PAM-N links (PAM3 through PAM16).
When to Use
System design and architecture exploration
Designing SerDes links for a target data rate, signaling scheme (NRZ, PAM4, PAM-N), and channel loss
Evaluating equalization architectures (FFE, CTLE, DFE) and optimizing tap settings
Sweeping design parameters to find optimal configurations
Using industry reference designs (PCIe, USB4, DDR5, CEI, UCIe) as starting points
Characterizing Tx/Rx analog effects (parasitic capacitance, rise time, termination impedance)
Building custom datapath blocks for nonstandard equalization
Channel modeling and characterization
Loading S-parameter Touchstone files into SerdesSystem
Modeling channels with loss profiles, crosstalk (FEXT/NEXT), and aggressors
Fitting CTLE transfer functions from measured data via
ctlefit
IBIS-AMI model generation
Building IBIS-AMI models for Tx, Rx, Redriver, or Retimer configurations
Exporting to Simulink, configuring AMI parameters, and compiling
.ami/.ibs/.dll/.soScripting Simulink simulations and parameter sweeps with
sim/parsim
Analysis and validation
Running statistical and time-domain simulations
Processing eye diagrams (eye height, eye width, COM, VEC, bathtub curves)
Decomposing jitter (TJ, RJ, DJ, DDJ, DCD, ISI)
Validating compiled AMI models against behavioral baselines
Running compliance checks with eye masks and jitter budgets
When NOT to Use
RF/microwave circuit design, antenna modeling, or baseband DSP filter design
General Simulink model scripting unrelated to SerDes
Must-Follow Rules
System Setup
SymbolTime / SampleInterval must yield an integer SamplesPerSymbol — fractional ratios cause silent errors
TxModel/RxModelareTransmitter/Receiverobjects — not cell arrays. Construct withTransmitter('Blocks', {block1, block2}).Transmitterrequires single-quoted property names —Transmitter("Blocks", ...)throws anismembererror.Receiverand all other classes accept double quotesSignal conversion functions require column vectors —
impulse2pulse,pulse2stateye, etc. error on row vectorsInclude
AnalogModelandJitterAndNoisefor realistic results — bareTransmitter/Receiverwithout analog models produce optimistic COM (1-2 dB higher). Seereference/equalization-tuning.mdfor parameter guidance (rise time, parasitic C, termination R)
Equalization
Set
WaveTypeexplicitly when using datapath blocks directly in MATLAB — Simulink sets this automatically, but MATLAB defaults to"Sample"Adapted DFE/CTLE parameters are in
results.outparams— NOT on the block object. Afteranalysis(),sys.RxModel.Blocks{k}.TapWeightsstill holds initial values. In system objects chains, adapted taps are the second output:[y, taps] = dfecdr(x)DFECDR Mode=0 is passthrough in Sample mode — DFE only applies with Mode≥1. Pre-load adapted taps from
outparamswith Mode=1 for instant convergence, or use Mode=2 with 10xEqualizationGain(9.6e-04) for self-converging chainsSet
Modulationon DFECDR for PAM-N in system objects chains — Simulink inherits it from the model workspace, but MATLAB defaults to 2 (NRZ). Without this, PAM4 DFE adaptation fails silently
Metrics and Waveforms
Metrics.summary.EWis in picoseconds (already scaled) — do NOT multiply by 1e12.EHis in volts. PAM-N returns N-1 values per metric (e.g., PAM4 → 3 eyes, PAM8 → 7 eyes)Channel impulse from
analysis()is in V/s — when usingfilter()for time-domain convolution, multiply bydt:filter(impulse * dt, 1, wave). Without scaling, amplitudes blow up by ~10^11pulse2waveoperates on the stimulus provided — the output modulation depends on the input pattern (NRZ or PAM-N)
AMI and Simulink
Init-Only models cannot adapt — if DFE taps or CDR converge at runtime, you need a Dual model (both Init and GetWave)
AMI validation requires Signal Integrity Toolbox —
serdes.AMIrunner and the AMI Simulink block need both SerDes Toolbox and Signal Integrity ToolboxAMI GetWave: call in a chunked loop —
serdes.AMIpassesBlockSize(default 1024) toAMI_GetWave, so onlyBlockSizesamples are processed per call. You must call the object in aforloop withBlockSize-length chunks. State is preserved between calls via the DLL memory handleAMI GetWave: set
SkipFirstBlock = falsewhen calling from MATLAB — the default (true) is for Simulink's internal signal buffering and causes the first block to pass through unprocessedAMI Init:
RowSizemust match impulse length —serdes.AMIcrashes MATLAB (process termination, no error) ifRowSizedoesn't matchnumel(impulse)AMI generation requires Simulink model — use
IbisAmiManagerGUI orserdes.AMIExportwithexport()programmatically (see Programmatic AMI Generation)
Workflow
Design Exploration
Most projects start here. The goal is to find the right equalization architecture and settings for your channel.
Design — Create a
SerdesSystemwith Tx/Rx blocks and channel (loss model or S-parameters)Analyze — Run
analysisfor statistical results,plotStatEyefor eye diagrams,analysisReportfor metricsSweep — Vary channel loss, FFE taps, CTLE gain, DFE taps, or jitter to map the design space
Compare — Evaluate architectures (FFE-only vs FFE+CTLE vs FFE+CTLE+DFE) using COM, eye height, eye width
Select — Choose the configuration that meets margin targets, then freeze equalization settings
Use SerdesSystem for programmatic exploration; serdesDesigner for interactive GUI work.
Waveform Processing
When you have a captured or imported waveform (e.g., from an oscilloscope or simulation) and want to equalize and analyze it directly:
Load — Import the waveform and define timing (
SampleInterval,SymbolTime)Equalize — Stream through datapath blocks (FFE, CTLE, DFECDR) with
WaveType = "Sample"Analyze — Build an eye diagram with
eyeDiagramSI, extract metrics (eye height, COM, VEC)Decompose jitter — Run
jitter()on the equalized waveform for TJ, RJ, DJ, DDJ, ISI breakdown
DFECDR and DFE require a sample-by-sample for loop in Sample mode; FFE and CTLE accept full vectors. See reference/waveform-processing.md for the Direct Equalization pattern.
IBIS-AMI Model Delivery
When you need compiled models (.ami/.ibs/.dll/.so) for EDA tools or IP delivery:
Export — Call
exportToSimulink(sys)to generate a Simulink model from the frozen designConfigure — Set AMI parameters, IBIS component/pin data, and model type via
IbisAmiManagerorserdes.AMIExportGenerate — Build
.ami/.ibsand compile.dll/.soviaserdes.AMIExportwithexport()Validate — Load compiled DLL/SO with
serdes.AMI, compare against behavioral reference (Init for impulse, GetWave for waveform)Cross-check — The Simulink path (
simwith Rx WaveOut) is the preferred time-domain reference. Compare against: statisticalanalysis(), system objects direct chain, and compiled AMI DLLs. Seereference/simulink-serdes-simulation.mdIterate — Fix discrepancies, re-export, re-validate until all paths agree
AMI Model Types
Choose the model type based on which equalization blocks need to adapt at runtime:
| Type | Init_Returns_Impulse | GetWave_Exists | Use For |
|---|---|---|---|
| Init-Only | true | false | LTI equalization (fixed FFE, CTLE). Supports statistical analysis |
| GetWave-Only | false | true | Time-domain only. No statistical analysis |
| Dual | true | true | Adaptive equalization (DFE, CDR). All analysis types |
Every IBIS-AMI model implements AMI_Init (required), AMI_GetWave (optional), and AMI_Close (required) per the IBIS standard.
Key Classes
| Class | Purpose |
|---|---|
SerdesSystem | Top-level system. Methods: analysis, plotStatEye, plotImpulse, plotPulse, plotAlignedPulse, plotWavePattern, analysisReport, exportToSimulink |
Transmitter | Tx container. Construct: Transmitter('Blocks', {serdes.FFE(...)}) |
Receiver | Rx container. Construct: Receiver('Blocks', {serdes.CTLE(...), serdes.DFECDR(...)}) |
ChannelData | Channel spec. Props: ChannelLossdB, ChannelLossFreq (default 5 GHz — must override to Nyquist), ChannelDifferentialImpedance, or Impulse/dt |
JitterAndNoise | IBIS 7.0 jitter/noise. 4 groups: Tx jitter (Rj/Dj/DCD/Sj), Rx jitter, Rx clock recovery (5 params, active with RxClockMode='clocked', 'convolved', or 'normal'), Rx noise. Values in seconds (default) or UI. See reference/serdes-api-reference.md |
serdes.AMI | Run compiled AMI DLLs/SOs. Call: [waveOut, impulseOut] = ami(waveIn, impulseIn, clockIn) |
serdes.AMIExport | Programmatic AMI export (R2026a+). Methods: export, getExportSettings. Props: ModelTypeTx, DLLFiles, LinuxCrossCompile |
SParameterChannel | S-parameter to impulse response. Handles .s4p through .s16p (multi-port returns Nx(K) matrix: col 1=thru, cols 2+=aggressors). Props: FileName, SampleInterval, StopTime, PortOrder |
eyeDiagramSI | Waveform eye diagram (R2024a+). Step: eyeObj(wave) — no output. Metrics: eyeHeight, eyeWidth, com, vec, margin |
ctlefit | CTLE pole/zero fitter. Import: ctlefit.readcsv. Output: GPZ matrix for serdes.CTLE("Specification", "GPZ Matrix") |
Datapath Blocks
| Block | Role | Mode Values | Key Properties |
|---|---|---|---|
serdes.FFE | Feed-forward equalizer | 0, 1 | TapWeights, TapSpacing, Normalize |
serdes.CTLE | Continuous-time linear EQ | 0, 1, 2 | Specification, DCGain, ACGain, PeakingGain, GPZ |
serdes.DFECDR | DFE + clock recovery | 0, 1, 2 | TapWeights, CDRMode, PhaseDetector, Count |
serdes.DFE | Standalone DFE | 0, 1, 2 | TapWeights, EqualizationGain, EqualizationStep |
serdes.CDR | Standalone CDR | 0, 1 | CDRMode, Count, Step, Sensitivity. Mode is deprecated |
serdes.AGC | Auto gain control | 0, 1 | TargetRMSVoltage, MaxGain, AveragingLength |
serdes.VGA | Variable gain amplifier | 0, 1 | Gain |
serdes.SaturatingAmplifier | Limiting amplifier | 0, 1 | Limit, LinearGain, Specification |
serdes.PassThrough | No-op placeholder | — | — |
Mode values: 0 = Fixed (not exported), 1 = Fixed (exported as AMI parameter), 2 = Adaptive (GetWave). Only CTLE, DFECDR, and DFE support Mode=2.
Set CTLE Specification before setting gain properties — using ACGain with the default spec triggers a warning. GPZ Matrix requires Mode=1 for ConfigSelect to switch rows. See reference/equalization-tuning.md for the 4 specification options.
Patterns
Each reference file includes executable code patterns for its topic. Load the relevant reference before writing code — it contains correct calling conventions, constructor arguments, and property names. See the References table in Conventions below for the full routing map.
Common Mistakes
See reference/common-mistakes.md for 37 documented anti-patterns with wrong/correct code pairs covering API misuse, wrong property names, silent failures, and performance pitfalls.
Conventions
Design
Use
SerdesSystemfor programmatic design;serdesDesignerfor interactive exploration. Always runanalysis/plotStatEyebefore exportingCRITICAL:
ChannelLossFreqdefaults to 5 GHz — always override to Nyquist (1/(2*SymbolTime)). Without this, 15 dB at 5 GHz becomes 40+ dB at Nyquist and the eye appears closed. UseSParameterChannelfor real channelsanalysis()does not auto-optimize FFE taps — use canonical de-emphasis withsum(abs(taps)) ≈ 1.0DFE taps initialized to zero are optimized by the statistical solver automatically
Industry reference designs:
serdesDesigneroropenExample('serdes/PCIe5TransmitterReceiverIBISAMIModelExample'). Seereference/equalization-tuning.mdfor starting-point configurations
Visualization and Metrics
All
SerdesSystemplot methods (plotStatEye,plotImpulse,plotPulse,plotAlignedPulse,plotWavePattern) render into the current axes — usetiledlayout/nexttileorsubplotfor multi-panel layoutseyeDiagramSI(R2024a+) for waveform eye diagrams;jitter(R2024b+) for jitter decompositionDFECDR/DFE in
"Sample"WaveType require scalar (sample-by-sample) input; FFE/CTLE accept full vectorsWhen generating PRBS waveforms via
pulse2wave, ask the user for PRBS order and symbol count — default PRBS-10 (1023 symbols) if unspecifiedUse
serdes.utilities.SignalIntegrityColorMap(nothot/parula) forpulse2stateyeplots. For compliance, usemargin(eyeObj, eyeMask)See
reference/visualization-and-metrics.mdfor all plot methods, metrics fields/units, eyeDiagramSI, and jitter
AMI Export and Validation
Use
serdes.AMIExportwithexport()for programmatic AMI generation — notslbuild(legacy, R2025b and earlier only)Validate compiled DLLs/SOs with
serdes.AMIbefore sending to EDA tools — both Init (impulse correlation) and GetWave (waveform chain)Simulink is the preferred time-domain path —
sim()with Rx WaveOut is the ground truth for adaptive systems. Compare against system objects chain and compiled AMI DLLs as cross-checks. Use the same PRBS stimulus across all time-domain pathsAMI export parameters on Simulink blocks use
*AMIsuffix ('ModeAMI','TapWeightsAMI'); model-level settings go throughIbisAmiManagerorserdes.AMIExportNo compiled DLLs/SOs ship with the toolbox. Cross-compile for Linux
.soviaserdes.utilities.createCrossCompiler
Script-First Workflow
For design, analysis, or sweep tasks — write code to .m files on disk, not inline MCP snippets. For quick one-off checks, inline evaluate_matlab_code is fine.
Create a
.mfile (e.g.,serdes_pam4_design.m), run viarun_matlab_file, iterate by editing and re-runningDeliver a polished script —
%% Parametersblock at top,%% Sectionheaders, summaryfprintfat end
References
| Load when... | Reference |
|---|---|
Building or modifying a SerdesSystem | reference/serdes-api-reference.md |
| Debugging wrong API names or silent failures | reference/common-mistakes.md |
| Working with S-parameter files or crosstalk | reference/channel-modeling.md |
| Tuning CTLE, FFE, DFE, or fitting transfer functions | reference/equalization-tuning.md |
| Sweeping parameters or running GA optimization | reference/optimization.md |
| Plotting eyes, reading metrics, or decomposing jitter | reference/visualization-and-metrics.md |
| Equalizing captured or imported waveforms directly | reference/waveform-processing.md |
| Exporting to Simulink, compiling AMI DLLs, or validating AMI models | reference/programmatic-ami-generation.md |
Scripting Simulink simulations or parsim sweeps | reference/simulink-serdes-simulation.md |
| Building custom datapath blocks | reference/custom-datapath-blocks.md |
| Looking up utility functions (SNR, ICN, resample) | reference/serdes-utilities.md |
Copyright 2026 The MathWorks, Inc.
Files
- ATTRIBUTION.mdAdded by AutoGineer1.0 KiB
- LICENSELicence file added by AutoGineer1.4 KiB
- SKILL.md16.4 KiB
- manifest.yaml1000 B
- reference/channel-modeling.md7.8 KiB
- reference/common-mistakes.md28.5 KiB
- reference/custom-datapath-blocks.md12.8 KiB
- reference/equalization-tuning.md11.4 KiB
- reference/optimization.md9.0 KiB
- reference/programmatic-ami-generation.md25.4 KiB
- reference/serdes-api-reference.md28.4 KiB
- reference/serdes-utilities.md10.4 KiB
- reference/simulink-serdes-simulation.md19.4 KiB
- reference/visualization-and-metrics.md20.0 KiB
- reference/waveform-processing.md7.8 KiB
reference/visualization-and-metrics.md
Visualization and Metrics
Plot methods, metrics extraction, eye diagram analysis, and jitter decomposition
for SerDes systems. Covers both statistical (analysis() path) and time-domain
(eyeDiagramSI path) workflows.
SerdesSystem Plot Methods
All plot methods render into the current axes. They work with subplot,
tiledlayout/nexttile, or standalone figure. All auto-call analysis() if
needed — no prerequisite.
| Method | What It Shows | X-Axis | Traces |
|---|---|---|---|
plotStatEye(sys) | Statistical eye heatmap + contours | ps | Image + N-1 eye contour pairs (PAM-N) |
plotImpulse(sys) | Impulse response | ns | Unequalized + Equalized (+ FEXT/NEXT with crosstalk) |
plotPulse(sys) | Pulse response | ns | Unequalized + Equalized |
plotAlignedPulse(sys) | Pulse aligned to cursor | UI | Unequalized + Equalized |
plotWavePattern(sys) | PRBS waveform | ns | Unequalized + Equalized |
analysisReport(sys) | Text summary to command window | — | Not a figure — prints EH/EW/COM/VEC + adapted params |
Multi-Panel Layouts
% tiledlayout — preferred for multi-panel
figure; tiledlayout(1, 2);
nexttile; plotStatEye(sysA); title("Config A");
nexttile; plotStatEye(sysB); title("Config B");
sgtitle("Side-by-Side Comparison");
% subplot works too
figure;
subplot(1, 2, 1); plotStatEye(sysA); title("Config A");
subplot(1, 2, 2); plotStatEye(sysB); title("Config B");
% All plot methods work the same way
figure; tiledlayout(2, 2);
nexttile; plotImpulse(sys); title("Impulse");
nexttile; plotPulse(sys); title("Pulse");
nexttile; plotAlignedPulse(sys); title("Aligned Pulse (UI)");
nexttile; plotWavePattern(sys); title("PRBS Waveform");Crosstalk Traces
With crosstalk enabled (ICN or multi-port S-parameters), plotImpulse adds
FEXT and NEXT traces automatically — 6 traces total:
Unequalized primary, Equalized primary
Unequalized FEXT, Equalized FEXT
Unequalized NEXT, Equalized NEXT
Other plot methods show only primary (victim) traces.
Metrics from analysis()
After analysis(sys), metrics are on sys.Metrics.summary:
| Field | Unit | Notes |
|---|---|---|
COMestimate | dB | Channel Operating Margin |
EH | volts | Eye Height — multiply by 1e3 for mV |
EW | picoseconds | Eye Width — already scaled, do NOT multiply by 1e12 |
VEC | percent | Vertical Eye Closure |
eyeLinearity | ratio | 1.0 = ideal (PAM4+) |
PAM-N returns N-1 values per metric (e.g., PAM4 returns 3 EH values, one per
eye). Index with EH(2) for the center (worst) eye.
results = analysis(sys);
m = sys.Metrics.summary;
fprintf("COM: %.2f dB, EH: %.1f mV, EW: %.1f ps\n", ...
m.COMestimate, m.EH(2)*1e3, m.EW(2));analysisReport Output
analysisReport(sys) prints a formatted text summary to the command window:
Eye Height/Width/Area per eye (Upper, Center, Lower for PAM4)
COM, VEC, eyeLinearity
Adapted parameter values (CTLE ConfigSelect, DFE TapWeights, CDR Phase)
Interior DFE state (thresholds, counters, frequency estimates)
optPulseMetric (LTI Only)
Computes metrics from the equalized pulse response — no jitter, no noise.
Use for quick LTI design exploration; use sys.Metrics.summary for
jitter-aware metrics.
results = analysis(sys);
eqPulse = results.pulse(:, 2); % column 2 = equalized
metrics = optPulseMetric(eqPulse, samplesPerSymbol, sampleInterval, targetBER);
fprintf("COM: %.1f dB, EH: %.1f mV, EW: %.1f ps\n", ...
metrics.maxCOM, metrics.maxEyeHeight * 1e3, metrics.eyeWidth * 1e12);eyeDiagramSI (R2024a+)
System object that builds a 2-D eye histogram from time-domain waveform data. Provides measurement methods for eye metrics, bathtub curves, and compliance testing.
Setup and Usage
eyeObj = eyeDiagramSI;
eyeObj.SampleInterval = dt; % sample interval (seconds)
eyeObj.SymbolTime = symbolTime; % symbol period (seconds)
eyeObj.Modulation = modulation; % 2 = NRZ, 4 = PAM4, etc.
eyeObj(waveform); % step — NO output captureCritical: Do NOT name the variable eye — it shadows MATLAB's built-in
eye() matrix function. Use eyeObj instead.
The step call has no output argument. It updates internal state (histogram). Capturing the output errors:
% WRONG — stepImpl has no output
result = eyeObj(waveform); % ERROR: Too many output arguments
% CORRECT — call without output, then read metrics
eyeObj(waveform);
eh = eyeHeight(eyeObj);Properties
| Property | Default | Description |
|---|---|---|
SampleInterval | 1 | Time between samples (seconds) |
SymbolTime | 8 | Symbol period (seconds) |
Modulation | 2 | Number of symbol levels |
ClockMode | "Auto" | Clock recovery: "Auto", "Clocked", "Ideal", "Convolved" |
PhaseDetector | "BangBang" | CDR algorithm: "BangBang", "BaudRateTypeA", "None" |
SymbolsPerDiagram | 2 | Eye width in symbol periods |
TimeBins | 255 | Histogram time resolution |
AmplitudeBins | 255 | Histogram voltage resolution |
AmplitudeLimits | [] | Voltage range [min max] — auto if empty |
SymbolThresholds | [] | Decision thresholds — auto for PAM4 |
Measurement Methods
| Method | Returns | Description |
|---|---|---|
eyeHeight(eye) | scalar or vector | Vertical eye opening (V). PAM-N returns N-1 values |
eyeWidth(eye) | scalar or vector | Horizontal eye opening (seconds) |
eyeArea(eye) | scalar or vector | Area of eye opening (V*s) |
com(eye) | scalar | Channel operating margin (dB) |
vec(eye) | scalar | Vertical eye closure (dB) — 20*log10(amplitude/eyeHeight). Note: different unit than Metrics.summary.VEC (percent) |
eyeLinearity(eye) | scalar | PAM4 eye linearity (1.0 = ideal) |
risetime(eye) | scalar | Rise time from eye diagram |
falltime(eye) | scalar | Fall time from eye diagram |
eyeCenter(eye) | struct | Center point coordinates |
eyeCrossing(eye) | struct | Crossing point coordinates |
eyeAmplitude(eye) | scalar | Peak-to-peak eye amplitude |
eyeLevels(eye) | struct | Symbol level statistics |
margin(eye, mask) | struct | Compliance margin against eyeMask |
bathtub(eye) | — | Plot bathtub curves |
slice(eye, ...) | vector | Extract histogram slice |
Plotting
figure;
plot(eyeObj); % 2-D eye diagram with colormap
title("Eye Diagram Title"); % title() works on the axeseyeDiagramSI vs pulse2stateye
| Feature | eyeDiagramSI | pulse2stateye |
|---|---|---|
| Input | Time-domain waveform | Pulse response (column vector) |
| Method | Histogram accumulation | Statistical convolution |
| Jitter | Captured from waveform | Not included (LTI only) |
| Clock recovery | Built-in CDR | None |
| Streaming | Can call step() multiple times | Single call |
| Metrics | Rich method set (COM, VEC, margins) | Use optPulseMetric separately |
| Compliance | margin() with eyeMask | Manual |
| Best for | Post-equalization waveform analysis | Quick LTI design exploration |
Custom Statistical Eye Rendering
Use pulse2stateye to build a statistical eye matrix for custom visualization:
results = analysis(sys);
eqPulse = results.pulse(:, 2);
[statEye, vAxis, tAxis] = pulse2stateye(eqPulse, samplesPerSymbol, modulation);
figure;
imagesc(tAxis * 1e12, vAxis * 1e3, statEye);
colormap(serdes.utilities.SignalIntegrityColorMap);
colorbar;
axis xy;
xlabel("Time (ps)"); ylabel("Voltage (mV)");
title("Statistical Eye");Colormap convention: Use serdes.utilities.SignalIntegrityColorMap (not
hot or parula) for statistical eye plots. Always add colorbar.
jitter (R2024b+)
Function that decomposes jitter from time-domain waveforms into standard IBIS/JEDEC jitter components.
Basic Usage
% With SampleInterval and SymbolTime (suppress plots for scripting)
J = jitter(waveform, SampleInterval=dt, SymbolTime=symbolTime, Plot=false);
% With explicit time vector
J = jitter(timeVec, waveform, SymbolTime=symbolTime, Plot=false);
% With reference waveform (for ISI separation) — plots enabled
J = jitter(timeVec, waveform, timeRef, waveRef, Plot="on");Built-In Jitter Plots
When Plot=true (default) or Plot="on", jitter() creates a 7-panel figure
showing PDF histograms for each jitter component: TJ, RJ, DJ, SJ, DDJ, DCD, ISI.
Each panel shows timing error on the x-axis and PDF estimate on the y-axis.
Set Plot=false in scripts to suppress automatic histogram figures. When running
in automated pipelines or MCP evaluation, always use Plot=false to avoid
unwanted figure windows.
Output Structure Fields
| Field | Unit | Description |
|---|---|---|
TJrms | seconds | Total jitter (RMS) |
TJpkpk | seconds | Total jitter (peak-to-peak) |
RJrms | seconds | Random jitter (RMS) |
DJrms | seconds | Deterministic jitter (RMS) |
DJpkpk | seconds | Deterministic jitter (peak-to-peak) |
DDJrms | seconds | Data-dependent jitter (RMS) |
DDJpkpk | seconds | Data-dependent jitter (peak-to-peak) |
DCDrms | seconds | Duty cycle distortion (RMS) |
DCDpkpk | seconds | Duty cycle distortion (peak-to-peak) |
ISIrms | seconds | Inter-symbol interference jitter (RMS) |
ISIpkpk | seconds | Inter-symbol interference jitter (peak-to-peak) |
SJa | seconds | Sinusoidal jitter amplitude |
SJf | Hz | Sinusoidal jitter frequency |
SJp | radians | Sinusoidal jitter phase |
Critical: Field names use rms/pkpk suffixes — not plain TJ, RJ, etc.
Name-Value Options
| Name | Default | Description |
|---|---|---|
SampleInterval | — | Sample period for uniformly sampled data |
SymbolTime | — | Symbol period |
SymbolThresholds | auto | Decision thresholds for edge detection |
Plot | true | Display jitter histogram plots |
Frequencies | 1 | Max sinusoidal jitter frequencies to detect |
PastSymbols | 31 | Symbols before edge for DDJ correlation |
FutureSymbols | auto | Symbols after edge for DDJ correlation |
DCDMethod | "oddeven" | DCD measurement: "oddeven" or "risefall" |
Sinusoidal Jitter Detection Limitation
The jitter() function's sinusoidal jitter (SJ) detection works best with a single
dominant SJ frequency. When multiple SJ frequencies are present, set Frequencies
to the expected count. With no sinusoidal jitter present, the detected SJa may
be nonzero but small — treat values below 0.01 UI as noise floor artifacts.
PRBS Waveform Generation
When generating time-domain waveforms via pulse2wave, the PRBS order and symbol
count are user-configurable. Default to PRBS-10 (1023 symbols) if unspecified, but
always offer the choice. Longer sequences improve jitter statistics but increase
computation time.
prbsOrder = 10;
nSymbols = 2^prbsOrder - 1; % 1023 symbols
prbsSeq = prbs(prbsOrder, nSymbols * samplesPerSymbol);
wave = pulse2wave(eqPulse, prbsSeq, samplesPerSymbol);When comparing across multiple analysis paths (statistical, Simulink sim(),
system objects chain, AMI DLL chain), use the same PRBS sequence and length
so results are directly comparable.
Compliance Testing
Use eyeMask with eyeDiagramSI.margin() for standards-based pass/fail testing:
% Create eye mask for compliance testing
mask = eyeMask;
mask.MaskType = "Hexagonal"; % or "Rectangular"
mask.EyeHeightThreshold = 15e-3; % 15 mV minimum eye opening
mask.EyeWidthThreshold = 0.3; % 0.3 UI minimum width
mask.SymbolTime = symbolTime;
% Run margin check against the mask
result = margin(eyeObj, mask);
fprintf("Margin: %.2f dB, Pass: %s\n", result.Margin, string(result.Pass));eyeMask defines the compliance region. margin() returns the distance (in dB) to
the mask boundary. Positive margin = pass; negative = fail.
For CDR jitter tolerance (JTOL) compliance testing, see the Simulink-based
simulinkJTOLController workflow (requires Simulink + Parallel Computing Toolbox).
Bathtub Curves
Bathtub curves show BER vs timing offset — the horizontal eye margin at each BER level.
% Generate bathtub curve from eyeDiagramSI
figure;
bathtub(eyeObj);
title("Bathtub Curve");bathtub(eyeObj) plots the bathtub curve directly. The curve shows BER on a log
scale (y-axis) vs timing offset in UI (x-axis), with the characteristic bathtub
shape where the minimum BER corresponds to the optimal sampling point.
COM Waterfall (Per-Stage Contribution)
To understand how each equalization stage contributes to COM, run analysis with progressively more EQ stages enabled:
% Build configurations with increasing equalization
configs = {
"Channel only", {serdes.PassThrough}, {};
"FFE only", {ffe}, {};
"FFE+CTLE", {ffe}, {ctle};
"FFE+CTLE+DFE", {ffe}, {ctle, dfecdr};
};
comValues = zeros(size(configs, 1), 1);
for k = 1:size(configs, 1)
sys = SerdesSystem(...
'TxModel', Transmitter('Blocks', configs{k, 2}), ...
'RxModel', Receiver('Blocks', configs{k, 3}), ...
'ChannelData', channel, ...
'SymbolTime', symbolTime, 'SamplesPerSymbol', 16, ...
'Modulation', modulation, 'BERtarget', 1e-6);
sys.ChannelData.ChannelLossFreq = 1 / (2 * symbolTime);
analysis(sys);
comValues(k) = sys.Metrics.summary.COMestimate;
end
% Plot waterfall
figure;
bar(comValues);
set(gca, "XTickLabel", configs(:, 1));
ylabel("COM (dB)"); title("EQ Stage Contribution");This reveals which stage provides the most margin improvement and whether any stage is counterproductive (e.g., CTLE at low loss or with PAM8+).
Advanced Visualization Patterns
3D Surface Plot (COM vs Two Parameters)
Visualize COM across a 2-D parameter sweep as a 3D surface:
% Sweep FFE pre-cursor and CTLE AC gain
preRange = -0.20:0.02:0;
acRange = 0:2:20;
comSurf = zeros(numel(acRange), numel(preRange));
for i = 1:numel(acRange)
for j = 1:numel(preRange)
pre = preRange(j);
main = 1 - abs(pre) - 0.15; % post fixed at -0.15
ffe = serdes.FFE("TapWeights", [pre main -0.15]);
ctle = serdes.CTLE("Specification", "DC Gain and AC Gain", ...
"DCGain", 0, "ACGain", acRange(i));
sysT = SerdesSystem('ChannelData', channel, ...
'TxModel', Transmitter('Blocks', {ffe}), ...
'RxModel', Receiver('Blocks', {ctle, dfecdr}), ...
'SymbolTime', symbolTime, 'SamplesPerSymbol', 16, ...
'Modulation', modulation);
sysT.ChannelData.ChannelLossFreq = 1 / (2 * symbolTime);
analysis(sysT);
comSurf(i, j) = sysT.Metrics.summary.COMestimate;
end
end
figure;
surf(preRange, acRange, comSurf);
xlabel("FFE Pre-Cursor"); ylabel("CTLE AC Gain (dB)"); zlabel("COM (dB)");
title("COM Surface: FFE Pre vs CTLE AC Gain");
colormap(turbo); colorbar;BER Contour Overlay on Eye Diagram
Overlay BER contour lines on an eye diagram using eyeDiagramSI:
% Build eye diagram
eyeObj = eyeDiagramSI;
eyeObj.SampleInterval = dt;
eyeObj.SymbolTime = symbolTime;
eyeObj.Modulation = modulation;
eyeObj(waveform);
% Plot with BER contours
figure;
plot(eyeObj);
hold on;
% Extract histogram and compute BER contours
hist2d = eyeObj.EyeHistogram;
totalHits = sum(hist2d(:));
berLevels = [1e-3, 1e-6, 1e-9, 1e-12];
contour(hist2d / totalHits, berLevels, "LineWidth", 1.5);
hold off;
title("Eye Diagram with BER Contours");DFE Tap Adaptation Trajectory
Track DFE tap convergence over time using the system objects chain:
% Setup DFECDR in adaptive mode
dfecdrAdapt = serdes.DFECDR("Mode", 2, "TapWeights", zeros(1, 5), ...
"SymbolTime", symbolTime, "SampleInterval", dt, ...
"Modulation", modulation, "WaveType", "Sample", ...
"EqualizationGain", 9.6e-4); % 10x default for faster convergence
% Stream sample-by-sample and record taps
nSamples = numel(waveform);
tapHistory = zeros(nSamples, 5);
for k = 1:nSamples
[~, taps] = dfecdrAdapt(waveform(k));
tapHistory(k, :) = taps;
end
% Plot adaptation trajectory
symbolIndices = (1:nSamples) / samplesPerSymbol;
figure;
plot(symbolIndices, tapHistory, "LineWidth", 1.2);
xlabel("Symbols"); ylabel("Tap Weight");
title("DFE Tap Adaptation Trajectory");
legend("Tap " + (1:5), "Location", "best");Statistical vs Time-Domain COM Discrepancy
Statistical COM (sys.Metrics.summary.COMestimate) and time-domain COM
(com(eyeObj)) measure the same concept but differ in methodology:
| Source | Method | Includes |
|---|---|---|
sys.Metrics.summary.COMestimate | Statistical convolution of pulse response | Optimal DFE taps, BER target, jitter model |
com(eyeObj) | Histogram analysis of time-domain waveform | Actual CDR recovery, finite PRBS, waveform jitter |
Expect 1-3 dB difference. Statistical COM is typically higher because it uses the optimal sampling phase and mathematically optimal DFE taps. Time-domain COM reflects real CDR tracking and finite adaptation convergence.
Use statistical COM for design-space exploration (fast, repeatable). Use time-domain COM for final validation (realistic, includes implementation effects).
Pattern: Side-by-Side Eye Comparison
% Compare two configurations side by side using tiledlayout
% plotStatEye renders into the current axes — works with subplot and tiledlayout
configs = {10, "10 dB"; 20, "20 dB"}; % {loss, label}
figure; tiledlayout(1, size(configs, 1));
for c = 1:size(configs, 1)
sys = SerdesSystem(...
'TxModel', Transmitter('Blocks', {serdes.FFE('TapWeights', [-0.1 0.7 -0.2])}, ...
'AnalogModel', AnalogModel('R', 50, 'C', 1e-13), ...
'RiseTime', 1e-11, 'VoltageSwingIdeal', 1), ...
'RxModel', Receiver('Blocks', ...
{serdes.CTLE('Specification', "DC Gain and AC Gain", ...
'DCGain', 0, 'ACGain', 12, 'PeakingFrequency', 14e9), ...
serdes.DFECDR('TapWeights', zeros(1, 5), 'Mode', 2)}, ...
'AnalogModel', AnalogModel('R', 50, 'C', 2e-13)), ...
'ChannelData', ChannelData('ChannelLossdB', configs{c, 1}, ...
'ChannelLossFreq', 14e9, 'ChannelDifferentialImpedance', 100), ...
'JitterAndNoise', JitterAndNoise('RxClockMode', 'clocked'), ...
'SymbolTime', 35.71e-12, 'SamplesPerSymbol', 16, ...
'Modulation', 4, 'Signaling', 'Differential', 'BERtarget', 1e-6);
results = analysis(sys);
m = sys.Metrics.summary;
nexttile; plotStatEye(sys);
title(sprintf("%s — COM %.1f dB, EH %.1f mV", configs{c, 2}, m.COMestimate, m.EH(2)*1e3));
for p = 1:numel(results.outparams)
if isstruct(results.outparams{p}) && isfield(results.outparams{p}, 'DFECDR')
fprintf("%s DFE taps: %s\n", configs{c, 2}, mat2str(results.outparams{p}.DFECDR.TapWeights, 3));
end
end
endAll SerdesSystem plot methods (plotStatEye, plotImpulse, plotPulse, plotAlignedPulse, plotWavePattern) render into the current axes. Use tiledlayout/nexttile or subplot for multi-panel layouts.
Pattern: Waveform Eye Diagram and Jitter Analysis
% Build eye diagram from time-domain waveform (R2024a+)
eyeObj = eyeDiagramSI;
eyeObj.SampleInterval = dt;
eyeObj.SymbolTime = symbolTime;
eyeObj.Modulation = modulation;
eyeObj(waveform); % step — NO output capture
% Read metrics
eh = eyeHeight(eyeObj); % PAM-N returns N-1 values (one per eye)
ew = eyeWidth(eyeObj);
comVal = com(eyeObj);
fprintf("Eye Height: %.1f mV, Eye Width: %.1f ps, COM: %.2f dB\n", ...
eh(1) * 1e3, ew(1) * 1e12, comVal);
% Jitter decomposition (R2024b+)
% Requires a long waveform (>200 symbols) — use pulse2wave with PRBS-10+
prbsSeq = prbs(10, (2^10 - 1) * samplesPerSymbol);
wave = pulse2wave(eqPulse, prbsSeq, samplesPerSymbol);
J = jitter(wave, SampleInterval=dt, SymbolTime=symbolTime, Plot=false);
fprintf("TJ: %.2f ps (pk-pk), RJ: %.2f ps (RMS), DDJ: %.2f ps (pk-pk)\n", ...
J.TJpkpk * 1e12, J.RJrms * 1e12, J.DDJpkpk * 1e12);Copyright 2026 The MathWorks, Inc.
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- matlab/matlab-agentic-toolkit · skills-catalog/rf-and-mixed-signal/matlab-model-serdes-systems · commit 2a7f215
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