Wafer Map MCP Server

An MCP (Model Context Protocol) server that exposes semiconductor wafer analysis tools to AI assistants. These tools are designed for semiconductor test data analysis, enabling AI agents to correctly generate key engineering visualization charts such as wafer maps, P-charts, trend plots, and statistical analysis figures from raw test data.

Preview

Binary Map	Property Map	P-Chart

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

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

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

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:

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

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

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)

Tech Stack

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

License

MIT

wafer-map-mcp