Emc

EMC Pre-Compliance Skill

Automated EMC risk analysis for KiCad PCB designs. Identifies the most common causes of EMC test failures using geometric rule checks, analytical emission formulas, and optional SPICE simulation.

This is a risk analyzer, not a compliance predictor. It catches ~70% of common EMC design mistakes before fabrication. It cannot guarantee FCC/CISPR compliance — only a calibrated measurement in an accredited lab can do that. But it can reduce the first-spin failure rate from ~50% toward ~20-30%, potentially saving $5K-$50K per avoided board respin.

Related Skills

Handoff guidance: Run the kicad skill's analyze_schematic.py and analyze_pcb.py first — this skill consumes their JSON output. Use --full on the PCB analyzer for best results (enables per-track coordinates for ground plane crossing, edge proximity, and return path checks). During a design review, run EMC analysis after the schematic/PCB analyzers, SPICE simulation, and thermal analysis, then incorporate EMC findings into the report.

Requirements

• Python 3.10+ — stdlib only, no pip dependencies
• Schematic analyzer JSON — from analyze_schematic.py --output
• PCB analyzer JSON — from analyze_pcb.py --full --output (recommended with --full)
• SPICE simulator (optional) — ngspice, LTspice, or Xyce for SPICE-enhanced PDN/filter checks. Auto-detected. Without one, analytical models run unchanged.

Workflow

Step 1: Run the analyzers

python3 <kicad-skill-path>/scripts/analyze_schematic.py design.kicad_sch --analysis-dir analysis/
python3 <kicad-skill-path>/scripts/analyze_pcb.py design.kicad_pcb --full --analysis-dir analysis/

Step 2: Run EMC analysis

Pass --analysis-dir analysis/ — the script auto-resolves schematic.json and pcb.json from the manifest's current run, and writes emc.json into the same folder so the manifest tracks it.

# Recommended: auto-resolve inputs from the current run
python3 <skill-path>/scripts/analyze_emc.py --analysis-dir analysis/

# Equivalent — explicit paths still accepted (and required if you want to
# point at a non-current run or override one input)
python3 <skill-path>/scripts/analyze_emc.py \
    --schematic analysis/<run_id>/schematic.json \
    --pcb analysis/<run_id>/pcb.json \
    --analysis-dir analysis/

# One-off JSON (bypasses the cache)
python3 <skill-path>/scripts/analyze_emc.py --schematic schematic.json --pcb pcb.json --output emc.json

# SPICE-enhanced (improved PDN and filter accuracy)
python3 <skill-path>/scripts/analyze_emc.py --analysis-dir analysis/ --spice-enhanced

# Select target standard
python3 <skill-path>/scripts/analyze_emc.py --analysis-dir analysis/ --standard cispr-class-b

# Select target market (sets all applicable standards)
python3 <skill-path>/scripts/analyze_emc.py --analysis-dir analysis/ --market eu

# Filter by severity
python3 <skill-path>/scripts/analyze_emc.py --analysis-dir analysis/ --severity high

# Human-readable text output
python3 <skill-path>/scripts/analyze_emc.py --analysis-dir analysis/ --text

Step 3: Interpret results

Read the JSON report and incorporate findings into the design review. Each finding has a severity, rule ID, description, and actionable recommendation. See "Interpreting Results" below.

What Gets Checked

44 rule IDs across 18 categories. Each rule has a specific threshold, rationale, and source citation — see references/pcb-emc-rules.md for full details.

Advisory outputs (not findings):

• Pre-compliance test plan — frequency band prioritization, interface risk ranking, near-field probe points
• Regulatory coverage — market-to-standards mapping, coverage matrix (what the tool checks vs what requires lab testing)

Output Format

{
  "summary": {
    "total_checks": 42,
    "critical": 2, "high": 5, "medium": 8, "low": 12, "info": 15,
    "emc_risk_score": 73
  },
  "target_standard": "fcc-class-b",
  "findings": [
    {
      "category": "ground_plane",
      "severity": "CRITICAL",
      "rule_id": "GP-001",
      "title": "Signal crosses ground plane void",
      "description": "Net SPI_CLK crosses a 3.2mm gap in GND on In1.Cu",
      "components": ["U3", "U7"],
      "nets": ["SPI_CLK"],
      "recommendation": "Route around the gap, or fill the void"
    }
  ],
  "per_net_scores": [
    {"net": "SPI_CLK", "score": 67, "finding_count": 3, "rules": ["GP-001", "CK-001", "BE-001"]}
  ],
  "test_plan": {
    "frequency_bands": [{"band": "30-88 MHz", "risk_level": "high", "source_count": 12}],
    "interface_risks": [{"connector": "J1", "protocol": "USB", "risk_score": 8}],
    "probe_points": [{"ref": "L1", "x": 45.2, "y": 32.1, "reason": "switching inductor"}]
  },
  "regulatory_coverage": {
    "market": "us",
    "applicable_standards": ["FCC Part 15 Class B"],
    "coverage_matrix": [{"standard": "...", "coverage": "partial", "note": "..."}]
  }
}

Severity Levels

Risk Score