Samsung is pursuing an ambitious vision of autonomous semiconductor fabrication (autonomous fab) that leverages multiple AI technologies, including LLMs, to enhance manufacturing processes. This case study explores their comprehensive approach to implementing LLMOps in a highly complex manufacturing environment. **Overview and Context** The semiconductor manufacturing process involves approximately 1,000 steps for 5nm technology nodes, with strict quality requirements demanding at least 80% yield to be profitable. The complexity of modern semiconductor manufacturing at 5nm, 4nm, and 3nm nodes makes traditional control methods insufficient, creating an opportunity for AI and LLM-based solutions. **Multi-Modal Foundation Model Approach** Samsung has developed a unique approach combining multiple data modalities: - Traditional time series data from manufacturing equipment sensors - Text data from engineering notes and documentation - Image data from wafer inspections and defect analysis - Process and equipment operational data - Knowledge graphs connecting different information sources Their system uses RAG (Retrieval Augmented Generation) and various LLM technologies to create a comprehensive control and analysis platform. The foundation model approach is particularly important because process conditions constantly change due to equipment status, maintenance timing, and shift differences, making static models insufficient. **Key Implementation Areas** *Equipment Monitoring and Control* The system processes complex sensor data streams that previously required extensive manual analysis. For example, in plasma etching processes, multiple parameters like reflected power and forward power need continuous monitoring. The LLM-based system can automatically: - Interpret complex multi-step processes - Identify relevant measurement windows - Detect subtle anomalies in sensor patterns - Make real-time control decisions *Knowledge Management and Engineering Support* A significant innovation is the system's ability to handle unstructured engineering notes and documentation. Engineers can now query the system in natural language about previous issues or errors, receiving contextually relevant responses based on historical data and documentation. This represents a major improvement over traditional database approaches that struggled with unstructured data. *Defect Analysis and Quality Control* The system incorporates advanced image analysis capabilities: - Automated wafer inspection and defect classification - Pattern recognition across thousands of potential defects - Root cause analysis combining visual and sensor data - Yield analysis and prediction *Process Control and Optimization* Samsung has implemented reinforcement learning for process control, showing particular success in RF matching optimization. Traditional methods required multiple iterations for matching, while the AI-based approach achieves optimal matching in 5-6 iterations. **Technical Challenges and Solutions** *Time Series Processing* The team evaluated multiple time series foundation models including PatchTST, TimeLLM, and TimeGPT. Current challenges include: - Handling step function behavior accurately - Dealing with time delays in predictions - Achieving sufficient correlation scores for practical use *Data Integration and Standardization* A major challenge has been handling data from different equipment vendors and systems. Samsung is working through semiconductor industry organization SEMI to establish data format standards, and has created their own cloud infrastructure for secure data sharing and model development. *Knowledge Graph Implementation* The system uses knowledge graphs to identify subtle relationships between process parameters. In one notable case, this approach helped identify equipment issues that traditional analysis had missed by revealing changes in the network structure of sensor relationships. **Results and Impact** While still evolving, the system has shown several significant achievements: - Successful automation of complex pattern recognition tasks - Reduced time for equipment matching and optimization - Improved anomaly detection capabilities - Better utilization of historical engineering knowledge **Future Directions** Samsung continues to work on: - Improving time series foundation models for microsecond-level data - Developing better data standardization approaches - Expanding reinforcement learning applications - Enhancing real-time processing capabilities The case demonstrates both the potential and challenges of implementing LLMOps in complex manufacturing environments. While some components are already providing value, others like real-time time series processing still require significant development. The multi-modal approach appears particularly promising for handling the diverse data types and complex relationships in semiconductor manufacturing.