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://mp-staging.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/equalization-tuning.md
Equalization Tuning
CTLE Configuration via poleZeroDefine
serdes.utilities.poleZeroDefine generates a GPZ (Gain-Pole-Zero) matrix from
DC Gain + AC Gain specifications, suitable for serdes.CTLE in "GPZ Matrix" mode.
% Generate GPZ matrix from gain specifications
nyquistFreq = baudRate / 2;
gpzMatrix = serdes.utilities.poleZeroDefine( ...
"PeakingFrequency", nyquistFreq, ...
"DCGain", [0 -1 -2 -3 -4 -5 -6], ...
"ACGain", 0);
% Returns Nx4 matrix: [Gain(dB) Pole1(Hz) Zero1(Hz) Pole2(Hz)]
ctle = serdes.CTLE("Specification", "GPZ Matrix", ...
"GPZ", gpzMatrix, ...
"Mode", 1, ... % REQUIRED for ConfigSelect to work
"ConfigSelect", 3); % Select row 3 (0-indexed)For fitting measured CTLE frequency response data (e.g., from CSV), use ctlefit
instead — see reference/serdes-api-reference.md for the ctlefit class API.
CTLE Specification Modes
| Mode | Properties Set | When to Use |
|---|---|---|
"DC Gain and Peaking Gain" | DCGain, PeakingGain | Quick exploration |
"DC Gain and AC Gain" | DCGain, ACGain | Datasheet specs |
"AC Gain and Peaking Gain" | ACGain, PeakingGain | Relative gain control |
"GPZ Matrix" | GPZ, Mode, ConfigSelect | Multi-config sweeps, compliance |
Key rule: Set Specification BEFORE setting gain properties. Post-hoc changes to
Specification may silently ignore previously set values.
ConfigSelect Requires Mode=1
ConfigSelect only works when Mode = 1 (fixed configuration). With the default
Mode = 0 (bypass/auto), row 0 is always used regardless of ConfigSelect value.
This is a silent failure — no error, no warning.
FFE Optimization: MMSE vs ZF Tradeoff
Two conceptual strategies for FFE tap weight selection:
| Strategy | Objective | Pros | Cons |
|---|---|---|---|
| Zero-Forcing (ZF) | Minimize ISI at sample points | Maximum ISI cancellation, best for high-SNR | Amplifies noise at low SNR, can over-equalize |
| MMSE | Minimize mean-squared error (ISI + noise) | Better noise performance, robust at low SNR | Slightly higher residual ISI |
When to use which:
ZF approach: Low-loss channels (< 15 dB), NRZ, high SNR — maximize pre/post tap magnitude
MMSE approach: High-loss channels (> 20 dB), PAM4, noisy links — keep main cursor higher
At mid-loss (15-20 dB), MMSE-like taps typically yield 0.5-1.0 dB better COM than ZF
SerDes Toolbox serdes.FFE does not have a built-in ZF/MMSE mode selector. Instead,
implement the tradeoff through tap weight selection:
% ZF-like taps: aggressive pre/post for maximum ISI cancellation
ffeZF = serdes.FFE("TapWeights", [-0.15 0.55 -0.30]);
% MMSE-like taps: conservative pre/post, preserves signal amplitude
ffeMMSE = serdes.FFE("TapWeights", [-0.08 0.77 -0.15]);Best practice: Use GA optimization (see reference/optimization.md) to find
optimal taps for the specific channel and noise environment. GA naturally finds the
MMSE-optimal point by maximizing COM (which includes both ISI and noise effects).
This is preferred over manually choosing ZF vs MMSE tap weights.
Multi-Lane Simulation
SerDes Toolbox simulates one lane at a time. For multi-lane links (e.g., PCIe x16, 100GBASE-CR4), loop over lanes individually:
% Multi-lane simulation — one lane at a time
sParamFiles = {"lane1.s4p", "lane2.s4p", "lane3.s4p", "lane4.s4p"};
comResults = zeros(numel(sParamFiles), 1);
for lane = 1:numel(sParamFiles)
spCh = SParameterChannel("FileName", sParamFiles{lane}, ...
"SampleInterval", symbolTime / 16);
channel = ChannelData("Impulse", spCh.ImpulseResponse, "dt", spCh.SampleInterval);
sys = SerdesSystem('ChannelData', channel, ...
'TxModel', Transmitter('Blocks', {ffe}), ...
'RxModel', Receiver('Blocks', {ctle, dfecdr}), ...
'SymbolTime', symbolTime, 'SamplesPerSymbol', 16, ...
'Modulation', modulation);
analysis(sys);
comResults(lane) = sys.Metrics.summary.COMestimate;
end
fprintf("Worst-lane COM: %.2f dB\n", min(comResults));Crosstalk between lanes is modeled via multi-port S-parameters on a per-victim basis,
not as a simultaneous multi-lane simulation. See reference/channel-modeling.md.
FFE Tap Count and Weight Selection
Tap Naming Convention
| Tap | Index | Role |
|---|---|---|
| Pre-cursor | 1 | Compensates pre-cursor ISI (negative weight) |
| Main cursor | 2 | Signal amplitude (positive, largest weight) |
| Post-cursor(s) | 3+ | Compensates post-cursor ISI (negative weights) |
A 3-tap FFE has [pre, main, post]. A 5-tap FFE has [pre2, pre1, main, post1, post2].
Normalization Constraint
FFE taps must satisfy sum(abs(taps)) = 1.0 (energy conservation). For a 3-tap FFE,
the main cursor is derived: main = 1 - |pre| - |post|.
Practical Guidelines
3 taps (1 pre + 1 post): Sufficient for most channels up to 25 dB loss
5 taps (2 pre + 2 post): Needed for channels > 25 dB or with significant reflections
7+ taps: Rarely needed; adds complexity without proportional benefit
Quantization: Real hardware quantizes taps (e.g., 0.02 resolution). Include in optimization.
Main cursor floor: Keep
main >= 0.4to avoid excessive signal attenuation
Tap Weight Starting Points by Loss
| Channel Loss | Pre | Main | Post | Notes |
|---|---|---|---|---|
| 5-10 dB | -0.05 | 0.90 | -0.05 | Minimal equalization needed |
| 10-20 dB | -0.10 | 0.70 | -0.20 | Moderate ISI compensation |
| 20-30 dB | -0.15 | 0.55 | -0.30 | Aggressive post-cursor emphasis |
| 30+ dB | -0.18 | 0.44 | -0.38 | Near main-cursor floor |
DFE Tap Count Selection
DFE taps cancel post-cursor ISI that FFE cannot reach. More taps = longer ISI cancellation, but diminishing returns after the channel impulse decays.
5 taps: Standard for most links up to 25 dB
10-15 taps: High-loss channels (25-30 dB) with long impulse tails
30-40 taps: Extreme cases (PAM8, very high loss) — last resort
Adaptive mode (
Mode = 2onserdes.DFECDR): Let the DFE find its own taps
Key finding: For PAM8 and higher modulation orders, DFE with many taps and NO CTLE (AC Gain = 0 dB) outperforms conventional CTLE + DFE. CTLE amplifies inter-level noise that is critical for PAM8's tight eye levels.
Tx/Rx Analog Parameter Guidance
Include AnalogModel on both Tx and Rx for realistic COM — omitting it inflates COM
by 1-2 dB. Analog parameters span three classes:
Default Values and Sensitivities
| Parameter | Set On | Default | Sensitivity | Notes |
|---|---|---|---|---|
RiseTime | Transmitter | 9.6 ps | High | Optimal: 0.3-0.4 x SymbolTime. Too fast or too slow hurts. |
C (Tx) | AnalogModel | 100 fF | High at > 500 fF | 1 pF → -2.5 dB COM, 2 pF → -7 dB |
C (Rx) | AnalogModel | 200 fF | High at > 500 fF | 1 pF → -2.3 dB COM, 2 pF → -5.5 dB |
R (Tx) | AnalogModel | 50 ohm | Low | +-0.5 dB over 25-100 ohm range |
R (Rx) | AnalogModel | 50 ohm | Low | +-0.5 dB over 25-100 ohm range |
VoltageSwingIdeal | Transmitter | 1.0 V | None (COM) | Scales EH linearly, COM is a ratio |
AnalogModel accepts only R (single-ended termination resistance) and C (single-ended
parasitic capacitance). RiseTime and VoltageSwingIdeal are Transmitter properties.
Setting Analog Parameters in SerdesSystem
sys = SerdesSystem("ChannelData", channel, ...
'TxModel', Transmitter('Blocks', {ffe}, ...
'AnalogModel', AnalogModel("R", 50, "C", 150e-15), ...
'RiseTime', 12e-12, 'VoltageSwingIdeal', 1.0), ...
'RxModel', Receiver('Blocks', {ctle, dfecdr}, ...
'AnalogModel', AnalogModel("R", 50, "C", 200e-15)));Design Insight: CTLE Counterproductive for High-Order PAM
At PAM8 and above, CTLE high-frequency boost amplifies inter-level noise more than it improves the eye. The optimal strategy is often AC Gain = 0 dB with many DFE taps.
56 Gbps PAM8 at 15 dB loss:
CTLE AC=12 dB + 5-tap DFE: COM = 0.8 dB (closed eye)
CTLE AC=0 dB + 40-tap DFE: COM = 4.9 dB (open eye)
This is the opposite of conventional NRZ/PAM4 wisdom where CTLE is essential.
Industry Reference Designs
Starting-point configurations for common standards. Adjust based on channel characteristics and margin requirements.
56 Gbps PAM4 (28 GBaud)
symbolTime = 1 / 28e9;
ffe = serdes.FFE("TapWeights", [-0.1 0.7 -0.2]);
ctle = serdes.CTLE("Specification", "DC Gain and AC Gain", ...
"DCGain", 0, "ACGain", 12, "PeakingFrequency", 14e9);
dfecdr = serdes.DFECDR("Mode", 2, "TapWeights", zeros(1, 5), ...
"SymbolTime", symbolTime, "Modulation", 4);112 Gbps PAM4 (56 GBaud)
symbolTime = 1 / 56e9;
ffe = serdes.FFE("TapWeights", [-0.15 0.55 -0.30]);
ctle = serdes.CTLE("Specification", "DC Gain and AC Gain", ...
"DCGain", 0, "ACGain", 15, "PeakingFrequency", 28e9);
dfecdr = serdes.DFECDR("Mode", 2, "TapWeights", zeros(1, 10), ...
"SymbolTime", symbolTime, "Modulation", 4);28 Gbps NRZ (28 GBaud)
symbolTime = 1 / 28e9;
ffe = serdes.FFE("TapWeights", [-0.05 0.85 -0.10]);
ctle = serdes.CTLE("Specification", "DC Gain and AC Gain", ...
"DCGain", 0, "ACGain", 8, "PeakingFrequency", 14e9);
dfecdr = serdes.DFECDR("Mode", 2, "TapWeights", zeros(1, 5), ...
"SymbolTime", symbolTime, "Modulation", 2);Fitting CTLE from Measured Data (ctlefit)
When you have measured CTLE frequency response data (e.g., from lab equipment or
vendor datasheets as CSV), use ctlefit to generate a GPZ matrix:
% Import measured CTLE data from CSV
% CSV format: freq,real,imag,real,imag,... (one column pair per config)
[f, H] = ctlefit.readcsv("ctle_measured.csv");
% Fit pole-zero model — returns object with GPZ property
obj = ctlefit("f", f, "H", H, "SampleInterval", dt, "MaxNumberOfPoles", 2);
truncateAbove(obj, 2 * nyquistFreq); % cut fitting noise above 2x Nyquist
% Extract GPZ matrix for SerDes CTLE
gpzMatrix = obj.GPZ;
% Use in SerdesSystem
ctle = serdes.CTLE("Specification", "GPZ Matrix", ...
"GPZ", gpzMatrix, "Mode", 1, "ConfigSelect", 0);ctlefit produces a GPZ matrix in the same [Gain(dB) Pole1(Hz) Zero1(Hz) Pole2(Hz)]
format as serdes.utilities.poleZeroDefine. Use ctlefit when fitting real measurements;
use poleZeroDefine when synthesizing from DC/AC gain specs. See
reference/serdes-api-reference.md for ctlefit properties and preprocessing methods.
Extracting Combined Equalization Transfer Function
To visualize the combined equalization transfer function programmatically:
% Get the combined equalization transfer function from a configured system
results = analysis(sys);
eqImpulse = results.impulse(:, 2); % equalized impulse
unEqImpulse = results.impulse(:, 1); % unequalized impulse
% Compute transfer function as ratio of FFTs
nfft = 2^nextpow2(numel(eqImpulse));
freq = (0:nfft/2-1) / (nfft * sys.dt);
H_eq = fft(eqImpulse, nfft);
H_uneq = fft(unEqImpulse, nfft);
H_combined = H_eq(1:nfft/2) ./ H_uneq(1:nfft/2);
figure;
plot(freq / 1e9, 20*log10(abs(H_combined)));
xlabel("Frequency (GHz)"); ylabel("Gain (dB)");
title("Effective Equalization Transfer Function");
xlim([0 freq(end)/2e9]);Note: This shows the combined equalization response (FFE + CTLE + DFE), not the CTLE alone. To isolate the CTLE, run two systems — one with CTLE enabled and one with CTLE bypassed (Mode=0) — and compare their impulse responses.
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- matlab/matlab-agentic-toolkit · skills-catalog/rf-and-mixed-signal/matlab-model-serdes-systems · commit 2a7f215
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- LICENSE (added by AutoGineer from LICENSE.md)
- ATTRIBUTION.md (written by AutoGineer)
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