FAQs: AI/ML Technologies

What do AI and ML stand for and how are they different?

AI is artificial intelligence and ML represents machine learning. Artificial intelligence represents the ability of computers to mimic human thought and tasks. Machine learning is a subcategory of AI. ML uses algorithms to learn from data, finding patterns and applying that understanding to decision making. Both are integral to achieving automation in semiconductor manufacturing.

AI and ML technologies are used by semiconductor manufacturers to solve quality, productivity and supply chain challenges using the next-generation approach of advanced and intelligent algorithms.

The semiconductor industry uses automated manufacturing to integrate multiple factory systems (such as those that track lots, processes, scheduling, and distribution) to improve the accuracy and quality of data and communication.

Automated manufacturing solutions can improve productivity and quality while reducing overall costs. Among the many ways they do so is by enabling improved collaboration in one factory or across sites, providing accurate insights into equipment states for better tool management, and managing scheduling solutions using real-time data.

Manufacturing simulation software can predict the impact of a particular change to the manufacturing process before it is made live in the production environment. This can identify potential problems to address prior to implementation, as well as help manufacturers find the most advantageous solutions.

Simulation software can model scenarios and generate synthetic data to use in AI training. Training models on rich datasets develop more robust, resilient models. Evaluating on-edge cases or other diverse scenarios that seldom occur in historical data increases generalization abilities of models, which improves accuracy overall.

Yes, simulation can overcome data collection challenges when implementing AI in semiconductor manufacturing. Simulation allows models to be trained on a rich, multi-year dataset, which aids prediction accuracy. Operational efficiency gains are then possible as planners are given the opportunity, in a non-production, simulated environment, to test and validate changes such as updating dispatching and scheduling parameters required for late lot predictions. Simulation also can find key performance indicator (KPI) differences by evaluating ML models versus existing dispatching rules or scheduling models. This provides the powerful opportunity to compare those differences.

FAQs: RAG

What is Retrieval-Augmented Generation (RAG)?

RAG is a hybrid AI approach that combines the generative power of large language models (LLMs) with the accuracy of document retrieval systems. It first searches a knowledge base for relevant data, then synthesizes and contextualizes that information to generate a natural language response.

RAG helps manufacturers by:

  • Reducing the risk of hallucinations (incorrect answers) by grounding responses in relevant documents.
  • Providing up-to-date, transparent answers with references to source materials.
  • Navigating complex, evolving information landscapes—like thousands of specs, SOPs, and tool logs—to surface what matters most.
The SmartFactory Genie assistant uses RAG to help engineers troubleshoot issues or onboard new tools. Instead of searching through manuals, an engineer can ask a question and receive a synthesized, context-aware answer with actionable steps, saving time and reducing friction in problem-solving and training.
  • RAG can be slower than purely generative models because it must search before generating a response.
  • Its effectiveness depends on the quality of the retriever—if relevant documents aren’t found, it may not provide an answer.
  • RAG is not designed for complex, multistep reasoning or autonomous task execution; it excels at answering questions and summarizing information.

RAG is advancing with innovations like multimodal retrieval (integrating text, images, and sensor data) and personalized retrieval tailored to user roles and history. These improvements will make RAG even more powerful and efficient for manufacturing environments.

Also, while RAG focuses on retrieving and summarizing the right information, Agentic AI builds on those foundations to plan, reason, and take action. As RAG systems grow more capable, they extend into agentic behaviors: detecting anomalies, analyzing context, and autonomously coordinating the right next steps.