DBSCAN Silicon Defect Detection

A Python project that detects silicon wafer defects using the DBSCAN clustering algorithm. This project aims to identify and visualise potential defect patterns based on wafer coordinate data.

Project Structure

• data/: Contains the dataset used for analysis and prediction.
• notebooks/: Jupyter notebooks for data analysis, feature engineering, and model building.
• README.md: Project overview and usage instructions.

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Features

• Load silicon wafer coordinate data from CSV files
• Apply DBSCAN clustering to detect potential defect patterns
• Automatically save clustering visualisations as images
• Adjustable DBSCAN parameters (eps and min_samples) for fine-tuning results

Tools & Libraries

• Python 3.10+
• Pandas - for data handling
• NumPy - for numerical operations
• Scikit-learn - for the DBSCAN clustering
• Matplotlib - for visualisation

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How to Use

1. Clone the repository:

   git clone https://github.com/nurulashraf/dbscan-silicon-defect-detection.git
   cd dbscan-silicon-defect-detection

2. Install the required libraries:

   pip install -r requirements.txt

3. Prepare your data

   Place your silicon wafer coordinate data in the data/ folder as silicon_defect_data.csv. The file should have two columns: x and y.

4. Run the defect detection:

   python dbscan_silicon_defect_detection.ipynb

5. Run the cells and explore the analysis.

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License

This project is licensed under the MIT License.