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.