PCIe 6.0 API Reference

The PCIe 6.0 module provides comprehensive support for PCI Express 6.0 validation with dual-mode NRZ/PAM4 capabilities.

Overview

The PCIe 6.0 implementation includes:

Complete PCIe 6.0 specification compliance (64 GT/s)
NRZ/PAM4 dual-mode support with seamless switching
Advanced link training with multi-phase optimization
Comprehensive compliance testing for electrical and timing parameters
Enhanced equalization algorithms (LMS, RLS, CMA, Decision-directed)
Multi-lane support (1-16 lanes) with skew analysis
Advanced eye diagram analysis with statistical modeling

Core Modules

Constants and Specifications

from serdes_validation_framework.protocols.pcie.constants import (
    SignalMode, PCIE_SPECS, PCIeSpecs, NRZSpecs, PAM4Specs
)

# Signal modes
mode = SignalMode.PAM4  # or SignalMode.NRZ

# Access specifications
pcie_specs = PCIE_SPECS['base']  # PCIeSpecs instance
nrz_specs = PCIE_SPECS['nrz']    # NRZSpecs instance
pam4_specs = PCIE_SPECS['pam4']  # PAM4Specs instance

SignalMode Enum

SignalMode.NRZ: Non-Return-to-Zero signaling
SignalMode.PAM4: 4-level Pulse Amplitude Modulation

Specifications

PCIeSpecs: Base PCIe 6.0 specifications (64 GT/s, timing parameters)
NRZSpecs: NRZ-specific parameters (amplitude, eye requirements)
PAM4Specs: PAM4-specific parameters (levels, EVM limits)

Mode Switching

from serdes_validation_framework.instrument_control.mode_switcher import (
    create_mode_switcher, ModeSwitcher, ModeConfig
)

# Create mode switcher
switcher = create_mode_switcher(
    default_mode=SignalMode.NRZ,
    sample_rate=100e9,
    bandwidth=50e9
)

# Switch modes
result = switcher.switch_mode(SignalMode.PAM4)
print(f"Switch successful: {result.success}")
print(f"Switch time: {result.switch_time*1000:.2f} ms")

ModeSwitcher Class

Methods:

switch_mode(target_mode, config=None): Switch to target signal mode
get_current_mode(): Get current signal mode
get_mode_config(mode): Get configuration for specific mode
update_mode_config(mode, config): Update mode configuration

Signal Analysis

from serdes_validation_framework.instrument_control.pcie_analyzer import (
    PCIeAnalyzer, PCIeConfig
)

# Create analyzer configuration
config = PCIeConfig(
    mode=SignalMode.PAM4,
    sample_rate=200e9,
    bandwidth=100e9,
    voltage_range=1.2,
    link_speed=64e9,
    lane_count=1
)

# Create analyzer
analyzer = PCIeAnalyzer(config)

# Analyze signal
signal_data = {
    'time': time_array,
    'voltage': voltage_array
}
results = analyzer.analyze_signal(signal_data)

Analysis Results

NRZ Mode Results:

level_separation: Voltage separation between levels
snr_db: Signal-to-noise ratio in dB
jitter_ps: Jitter measurement in picoseconds

PAM4 Mode Results:

min_level_separation: Minimum separation between PAM4 levels
rms_evm_percent: RMS Error Vector Magnitude percentage
snr_db: Signal-to-noise ratio in dB

Link Training

from serdes_validation_framework.protocols.pcie.link_training import (
    create_nrz_trainer, create_pam4_trainer, LinkTrainer
)

# Create trainer
trainer = create_pam4_trainer(
    target_ber=1e-12,
    max_iterations=1000
)

# Run training
result = trainer.run_training(signal_data)
print(f"Training success: {result.success}")
print(f"Final BER: {result.final_ber:.2e}")
print(f"Iterations: {result.iterations}")

Training Results

success: Boolean indicating training convergence
final_ber: Final bit error rate achieved
iterations: Number of training iterations
equalizer_coeffs: Final equalizer coefficients
snr_history: SNR progression during training

Equalization

from serdes_validation_framework.protocols.pcie.equalization import (
    create_lms_equalizer, create_rls_equalizer, AdaptiveEqualizer
)

# Create equalizer
equalizer = create_lms_equalizer(
    num_forward_taps=11,
    num_feedback_taps=5,
    step_size=0.01
)

# Run equalization
result = equalizer.equalize_signal(distorted_signal)
print(f"Converged: {result.converged}")
print(f"Final MSE: {result.final_mse:.6f}")

# Apply equalization
equalized_signal = equalizer.apply_equalization(input_signal)

Equalization Algorithms

LMS (Least Mean Squares): Fast convergence, good for stationary signals
RLS (Recursive Least Squares): Better tracking, higher computational cost
CMA (Constant Modulus Algorithm): Blind equalization
Decision-Directed: Uses symbol decisions for adaptation

Compliance Testing

from serdes_validation_framework.protocols.pcie.compliance import (
    ComplianceTestSuite, ComplianceConfig, ComplianceType
)

# Create test configuration
config = ComplianceConfig(
    test_pattern="PRBS31",
    sample_rate=200e9,
    record_length=100e-6,
    voltage_range=2.0,
    test_types=[ComplianceType.ELECTRICAL, ComplianceType.TIMING]
)

# Create test suite
test_suite = ComplianceTestSuite(config)

# Run compliance tests
results = test_suite.run_compliance_tests(time_data, voltage_data)

# Check overall status
overall_pass = test_suite.get_overall_status()

Compliance Test Types

ComplianceType.ELECTRICAL: Voltage levels, swing, common mode
ComplianceType.TIMING: Unit interval, jitter, eye opening
ComplianceType.PROTOCOL: Protocol-specific compliance
ComplianceType.FULL: All compliance tests

Test Sequences

from serdes_validation_framework.test_sequence.pcie_sequence import (
    create_single_lane_nrz_test, create_multi_lane_pam4_test,
    create_comprehensive_pcie_test
)

# Single lane test
test_sequence = create_single_lane_nrz_test(
    lane_id=0,
    sample_rate=100e9,
    bandwidth=50e9
)

# Multi-lane test
multi_lane_test = create_multi_lane_pam4_test(
    num_lanes=4,
    sample_rate=200e9,
    bandwidth=100e9
)

# Run complete test sequence
signal_data = {0: {'time': time_array, 'voltage': voltage_array}}
result = test_sequence.run_complete_sequence(signal_data)

Test Phases

TestPhase.INITIALIZATION: Signal quality verification
TestPhase.LINK_TRAINING: Adaptive link training
TestPhase.COMPLIANCE: Compliance testing
TestPhase.STRESS_TEST: Environmental stress testing
TestPhase.VALIDATION: Final validation and cross-lane analysis

Advanced Eye Diagram Analysis

from serdes_validation_framework.data_analysis.eye_diagram import (
    create_nrz_eye_analyzer, create_pam4_eye_analyzer, AdvancedEyeAnalyzer
)

# Create eye analyzer
eye_analyzer = create_pam4_eye_analyzer(
    symbol_rate=32e9,
    samples_per_symbol=32
)

# Analyze eye diagram
eye_result = eye_analyzer.analyze_eye_diagram(time_data, voltage_data)

# Access results
print(f"Eye height: {eye_result.eye_height:.3f} V")
print(f"Eye width: {eye_result.eye_width*1e12:.1f} ps")
print(f"Q-factor: {eye_result.q_factor:.2f}")

Eye Analysis Features

Statistical eye modeling: Density-based eye diagram generation
Jitter decomposition: Random, deterministic, periodic, data-dependent
Bathtub curves: Timing and voltage bathtub curve generation
Eye contour analysis: Multi-level contour analysis
Eye mask compliance: Automated mask violation detection

Usage Examples

Basic PCIe Validation Workflow

import numpy as np
from serdes_validation_framework.protocols.pcie.constants import SignalMode
from serdes_validation_framework.instrument_control.pcie_analyzer import PCIeAnalyzer, PCIeConfig
from serdes_validation_framework.protocols.pcie.link_training import create_pam4_trainer
from serdes_validation_framework.protocols.pcie.compliance import ComplianceTestSuite, ComplianceConfig, ComplianceType

# 1. Configure analyzer
config = PCIeConfig(
    mode=SignalMode.PAM4,
    sample_rate=200e9,
    bandwidth=100e9,
    voltage_range=1.2,
    link_speed=64e9,
    lane_count=1
)

analyzer = PCIeAnalyzer(config)

# 2. Analyze signal quality
signal_data = {'time': time_array, 'voltage': voltage_array}
analysis_results = analyzer.analyze_signal(signal_data)
print(f"SNR: {analysis_results['snr_db']:.1f} dB")

# 3. Run link training
trainer = create_pam4_trainer(target_ber=1e-12)
training_result = trainer.run_training(signal_data)
print(f"Training converged: {training_result.success}")

# 4. Compliance testing
compliance_config = ComplianceConfig(
    test_pattern="PRBS31",
    sample_rate=200e9,
    record_length=100e-6,
    voltage_range=2.0,
    test_types=[ComplianceType.FULL]
)

test_suite = ComplianceTestSuite(compliance_config)
compliance_results = test_suite.run_compliance_tests(
    signal_data['time'], signal_data['voltage']
)
print(f"Compliance: {'PASS' if test_suite.get_overall_status() else 'FAIL'}")

Multi-Lane Analysis

from serdes_validation_framework.test_sequence.pcie_sequence import create_multi_lane_pam4_test

# Create 4-lane test
test_sequence = create_multi_lane_pam4_test(num_lanes=4)

# Prepare multi-lane data
multi_lane_data = {
    0: {'time': time_array_0, 'voltage': voltage_array_0},
    1: {'time': time_array_1, 'voltage': voltage_array_1},
    2: {'time': time_array_2, 'voltage': voltage_array_2},
    3: {'time': time_array_3, 'voltage': voltage_array_3}
}

# Run complete validation
result = test_sequence.run_complete_sequence(multi_lane_data)

# Check results
print(f"Overall status: {result.overall_status.name}")
print(f"Total duration: {result.total_duration:.2f} seconds")

# Lane-specific results
for lane_id, metrics in result.lane_results.items():
    print(f"Lane {lane_id} performance score: {metrics.get('performance_score', 0):.1f}")

Performance Characteristics

Signal Analysis: < 1 second for 10K samples
Mode Switching: < 10 milliseconds
Link Training: < 5 seconds for convergence
Compliance Testing: < 3 seconds for full suite
Eye Diagram Analysis: < 2 seconds for complete analysis

Error Handling

All PCIe modules include comprehensive error handling:

Type validation: Runtime checking of all parameters
Graceful degradation: Fallback algorithms for edge cases
Detailed error messages: Clear indication of failure causes
Recovery mechanisms: Automatic retry and alternative methods

Thread Safety

The PCIe modules are designed to be thread-safe for concurrent analysis of multiple lanes or signals.