https://github.com/nurulashraf/dbscan-silicon-defect-detection
This project uses machine learning to detect anomalies in silicon wafers. It employs DBSCAN clustering and a Gradio interface for user interaction, enabling automated defect detection and enhancing quality control in semiconductor manufacturing
https://github.com/nurulashraf/dbscan-silicon-defect-detection
anomaly-detection clustering data-science dbscan gradio interactive-visualizations machine-learning python
Last synced: 2 months ago
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This project uses machine learning to detect anomalies in silicon wafers. It employs DBSCAN clustering and a Gradio interface for user interaction, enabling automated defect detection and enhancing quality control in semiconductor manufacturing
- Host: GitHub
- URL: https://github.com/nurulashraf/dbscan-silicon-defect-detection
- Owner: nurulashraf
- License: mit
- Created: 2025-04-26T13:50:10.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-27T20:16:24.000Z (about 1 year ago)
- Last Synced: 2025-05-06T16:05:16.647Z (about 1 year ago)
- Topics: anomaly-detection, clustering, data-science, dbscan, gradio, interactive-visualizations, machine-learning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 54.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
---
## 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
---
## How to Use
1. **Clone the repository:**
```bash
git clone https://github.com/nurulashraf/dbscan-silicon-defect-detection.git
cd dbscan-silicon-defect-detection
```
2. **Install the required libraries:**
```bash
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`.
5. **Run the defect detection:**
```bash
python dbscan_silicon_defect_detection.ipynb
```
6. Run the cells and explore the analysis.
---
## License
This project is licensed under the [MIT License](LICENSE).