Wafer Map

An MCP (Model Context Protocol) server that exposes semiconductor wafer analysis tools to AI assistants such as Claude Desktop.

Given a wafer test data file (CSV or ZIP), it renders:

• Yield summary statistics
• Binary pass/fail wafer map
• Continuous-value PIN property heatmaps
• Normal probability plots (P-charts) per wafer

All tools are accessible via a single HTTP endpoint, so any MCP-compatible client can use them.

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Preview

Binary Map	Property Map	P-Chart

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Tools

Tool	Description
run_wafer_analysis	Full analysis in one call: summary + binary map + all PIN maps + P-charts
get_wafer_info	Basic wafer summary (yield, pass/fail counts, PIN columns)
plot_wafer_bin	Binary pass/fail wafer map (BIN=0 → teal, else → black)
plot_wafer_property	Continuous-value heatmap for a single PIN column (blue → red)
plot_pchart	Normal probability plot per wafer for a PIN column

Data Format

CSV or ZIP (containing exactly one CSV) with columns:

BIN, X, Y, WAFER_ID, PIN_1, PIN_2, ..., PIN_N

• BIN = 0 → pass, otherwise fail
• X, Y → die coordinates on the wafer grid
• PIN_* → continuous measurement values

Colour Scale (IQR Robust Sigma)

Property maps and P-chart boundaries use IQR-based bounds to make subtle variations visible:

sigma  = (P75 - P25) / 1.35
IQR_L  = P50 - 6 × sigma
IQR_H  = P50 + 6 × sigma

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Quick Start

Option A: Docker (recommended)

docker build -t wafer-mcp .
docker run -p 8001:8001 wafer-mcp

The server is now available at http://localhost:8001/mcp.

To analyze your own data files, mount a volume:

docker run -p 8001:8001 -v /absolute/path/to/data:/data wafer-mcp
# then pass file_path="/data/your_wafer.zip" when calling tools

Option B: Local Python

Requirements: Python 3.10+

pip install -r requirements.txt
python server.py

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Sample Data

A sample dataset is bundled with the project at sample_data/sample_1.zip.

Location	Path
Local	./sample_data/sample_1.zip
Docker	/app/sample_data/sample_1.zip

Quick smoke test (Docker):

# inside the container the sample lives at /app/sample_data/sample_1.zip
# call any tool with this file_path to verify everything works

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Claude Desktop Configuration

The server uses Streamable HTTP transport, so use the url form in claude_desktop_config.json:

{
  "mcpServers": {
    "wafer-map": {
      "url": "http://localhost:8001/mcp"
    }
  }
}

Steps:

1. Start the MCP server (local or Docker)
2. Add the config above to Claude Desktop
3. Restart Claude Desktop
4. Ask Claude to analyze a wafer data file — it will automatically pick the right tool

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Tool Parameters

run_wafer_analysis

Param	Type	Default	Description
file_path	str	required	Path to .csv or .zip file
pin_columns	list[str] | None	None	Subset of PIN columns to plot; None = all
target_size	int	300	Output image pixel size

get_wafer_info

Param	Type	Default	Description
file_path	str	required	Path to .csv or .zip file

plot_wafer_bin

Param	Type	Default	Description
file_path	str	required	Path to .csv or .zip file
target_size	int	300	Output image pixel size

plot_wafer_property

Param	Type	Default	Description
file_path	str	required	Path to .csv or .zip file
pin_column	str	"PIN_1"	PIN column to visualise
target_size	int	450	Output image pixel size
data_l	float | None	None	Override lower bound of colour scale
data_h	float | None	None	Override upper bound of colour scale

plot_pchart

Param	Type	Default	Description
file_path	str	required	Path to .csv or .zip file
pin_column	str	"PIN_1"	PIN column to plot
target_size	int	300	Output image pixel size

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Project Structure

.
├── server.py                    # MCP server entry point
├── requirements.txt             # Python dependencies
├── Dockerfile                   # Container definition
├── sample_data/
│   └── sample_1.zip             # Bundled sample wafer dataset
├── tools/
│   ├── workflow/
│   │   └── analyze_wafer.py     # Orchestrates full analysis
│   ├── information_read/
│   │   └── read_wafer_info.py   # Parse CSV/ZIP and compute yield
│   ├── wafer_map/
│   │   ├── wafer_bin_binary_plot.py    # Binary map renderer (PySide6)
│   │   └── wafer_item_property_plot.py # Property heatmap renderer (PySide6)
│   └── statistic_plot/
│       └── pchart_plot.py       # P-chart renderer (matplotlib)
└── pchart/
    └── PchartReportWidget.py    # Legacy Qt widget (reference only)

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Tech Stack

• MCP: FastMCP — Streamable HTTP transport
• Wafer map rendering: PySide6 offscreen QPainter
• P-chart rendering: matplotlib + scipy + statsmodels

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License

MIT