iamamanporwal / semiconfab Goto Github PK

Semicon Fab Technologies- Cleanroom system Architecture

This project implements an AI-powered decontamination system for a cleanroom environment in the semiconductor industry. Here's a breakdown of the essential hardware components:

• Industrial PC (IPC):
  • High reliability
  • Wide operating temperature range
  • Vibration resistance
  • Dustproof casing
  • Consider brands specializing in industrial automation.
• Particle Sensors (Multiple):
  • PVE201 PARTICULATE MATTER & AIR QUALITY TRANSMITTER (or equivalent)
  • High sensitivity
  • Real-time data output
  • Network connectivity
  • Additional sensors for comprehensive coverage (if required)
• Decontamination System:
  • Air filtration unit
  • UV germicidal lamp system
  • Control interface compatible with chosen communication protocol
• Industrial-grade Ethernet Switch:
  • Reliable communication between devices
  • PoE (Power over Ethernet) for specific sensors
  • Hardened construction

Optional Hardware:

• Air Quality Sensors: Temperature, humidity, VOCs monitoring.
• Cameras (optional): Visual inspection and AI-powered image analysis.
  • Industrial cameras with sealed housings.
• Uninterruptible Power Supply (UPS): Power backup for cleanroom integrity.

Software:

• AI Software: Custom-developed or pre-trained solution for particle detection/classification.
• Industrial Automation Software: Data acquisition, decontamination control, visualization.

Communication Protocol:

• Modbus RTU, Profinet, EtherCAT (ensure compatibility).

Data Security:

• Secure data transmission protocols and access controls.

Scalability:

• Consider modular or upgradable hardware for future expansion.

This section outlines a possible Machine Learning (ML) system design for the project:

1. Data Acquisition:

• Real-time particle count data from PVE201 and potentially additional sensors.
• Preprocessing (cleaning, normalization).

2. Anomaly Detection Model:

• Anomaly detection algorithms (Isolation Forest, LOF, OCSVM).
• Learns "normal" particle behavior and flags deviations as potential contamination.
• Training data: Historical data of particle counts representing "clean" conditions with labeled contamination events (if available).

3. Decontamination Activation Logic:

• Thresholding on anomaly score for decontamination activation.
• Spatial reasoning (clustering/geospatial analysis) for pinpointing contamination location (if multiple sensors).

4. Model Training and Deployment:

• Train-Test Split for model evaluation.
• Continuous learning mechanisms for model adaptation.
• Deployment on Industrial PC for real-time processing and decontamination triggering.

• Anomaly Detection for Particle Sensor Data in Cleanrooms Using Isolation Forest: link to paper 1: https://www.mdpi.com/1424-8220/20/19/5650
• A Machine Learning Based Approach for Cleanroom Monitoring and Fault Detection: link to paper 2: https://ieeexplore.ieee.org/document/9332962
• Real-time Anomaly Detection with Local Outlier Factor: link to paper 3: https://ieeexplore.ieee.org/document/9439459

• Timestamp
• Sensor ID
• Particle Count
• (Optional) Environmental Data (Temperature, Humidity, VOCs)
• (Optional) Label (for training: "normal" or "contamination event")

Further Considerations:

• Design adjustments based on chosen model and cleanroom complexity.
• Feature engineering for improved model performance.
• Visualization tools for real-time data and model predictions.

This is a foundational outline. Further research and experimentation are recommended to tailor the solution to your specific needs.