Head of Solutions and Business Development, Semiconductor Amazon Web Services oakland, CA, United States
The semiconductor industry is entering a new era, marked by soaring global demand, shrinking device geometries, and increasingly intricate manufacturing processes. As complexity grows, traditional automation and human-driven process control are struggling to keep pace with the demands for higher yield, faster time-to-market, and resilient supply chains. Autonomous AI agents—intelligent, adaptive software entities capable of perceiving, reasoning, and acting within manufacturing environments—are emerging as a transformative solution to these challenges.
This presentation explores how the strategic deployment of autonomous AI agents, enabled by AWS’s advanced cloud, edge, and AI technologies, is reshaping semiconductor manufacturing and accelerating the evolution toward truly “smart” fabs. Leveraging AWS’s secure and scalable infrastructure, these agents utilize real-time data from thousands of sensors and equipment across the fab floor, applying advanced machine learning and generative AI models to continuously monitor, analyze, and optimize manufacturing operations. By integrating digital twin technology on AWS, AI agents can simulate process scenarios, evaluate potential interventions, and autonomously implement corrective actions, all while maintaining transparency and human oversight.
A central use case we will discuss is the application of AI agents for anomaly detection and root cause analysis, powered by AWS’s robust analytics and AI capabilities. In today’s fabs, these agents can analyze vast streams of heterogeneous data, identifying subtle patterns and deviations that may escape traditional monitoring systems. When anomalies are detected, AWS-enabled AI agents not only flag the issue in real time but also perform rapid root cause analysis—tracing problems back through complex process dependencies to pinpoint the underlying source. This capability enables faster, more accurate interventions, reducing unplanned downtime and minimizing the risk of defective output.
In addition to anomaly detection and root cause analysis, we will examine how AWS-powered autonomous AI agents are driving improvements in predictive maintenance and adaptive process control. By forecasting equipment failures and dynamically adjusting process parameters, these agents help maximize equipment uptime, optimize yield, and reduce operational costs. The architecture supporting these agents on AWS is designed for scalability, security, and compliance, ensuring robust performance from edge to cloud while protecting sensitive intellectual property.
As the industry moves toward fully autonomous, “lights-out” manufacturing, AWS’s AI agent solutions provide a practical and scalable pathway to realizing the vision of Industry 4.0 in semiconductor production. This presentation will offer actionable insights and a strategic blueprint for semiconductor leaders seeking to harness the power of AI agents with AWS, driving the next wave of productivity, quality, and innovation in chip manufacturing.
