Design a Python automation framework for semiconductor equipment retrofitting

Keynote

Abstract

In modern semiconductor fabs, retrofitting existing equipment with data interfaces, real-time monitoring, and flexible control capabilities presents an increasingly pressing challenge. This session will share an automation framework designed around Python for the intelligent retrofitting of legacy equipment. The framework integrates device communication protocols such as SECS/GEM and MCProtocol, paired with AI vision recognition modules and data analysis workflows for predictive maintenance and process optimization. The overall architecture adopts a microservices design, utilizing Django for the backend and Vue.js for the frontend, enabling seamless integration with MES, FDC, or production line systems. Although still in development, the framework already meets practical manufacturing requirements, offering a viable solution for achieving smart manufacturing through open-source technologies.

Details

Outline

  • Why Semiconductor Equipment Needs Modernization? Introduction to On-Site Pain Points
  • Core Architecture Design: Modularity, Scalability, AI Readiness
  • Framework Technical Components:
    • Communication Layer (SECS/GEM, Modbus, MCProtocol)
    • AI Modules (e.g., Defect Detection, Image Analysis)
    • Data Processing & Transmission (Redis, SQLite/PostgreSQL, InfluxDB)
    • User Interface (Django REST + Vue.js Single-Page Application)
  • Sample Workflow: From Equipment Data to Decision Recommendations
  • Integration Methods with MES and FDC Systems
  • Challenges and Reflections During Implementation
  • Q&A

Target Audience

  • Python developers interested in industrial automation
  • Engineers exploring AI applications in manufacturing
  • System integration engineers in semiconductor or electronics manufacturing
  • Technical decision-makers considering open-source technologies for equipment upgrades