Webinar on AI Agents for Root Cause Analysis in Semiconductors

A webinar dedicated to semiconductor factories has been announced to present root cause analysis methods supported by agentic AI. The event includes a multi-domain demonstration with Spotfire Industry Pro and a program focused on reducing investigation times in the face of massive data volumes.

The event targets engineers, operations, and data from fabs

The event is aimed at yield, process, and integration engineers, manufacturing operations managers, as well as quality and reliability engineers. It also targets data and analytics managers supporting wafer fabs, foundries, OSAT, and IDM, with the goal of identifying issues more quickly while maintaining decision-making confidence. The webinar is set to demonstrate how semiconductor teams accelerate root cause investigations, extend analysis to very large datasets, and transform disconnected data into actionable insights for manufacturing. Registration requires reserving a spot.

The session highlights agentic AI, dedicated views, and remote computing

The program showcases an analytics platform designed for semiconductors to help engineers connect information across domains without moving data. Agentic AI is presented as a lever to accelerate investigations into yield gaps and process issues. Sector-specific visualizations and remote computing are also emphasized to shorten analyses, including those involving billions of data points.

A live demonstration with Spotfire Industry Pro is planned

A live demonstration will show how to conduct a multi-domain root cause investigation using Spotfire Industry Pro. The program includes explanations of how the fragmentation of manufacturing data into silos can hinder yield recovery and increase costs, as well as a presentation of how agentic AI supports the automation of analysis across different domains and the creation of visualizations. Insights will be provided on applying high-performance analysis to large volumes of production data. This approach is set against a backdrop where the solution to a yield incident is rarely found in a single system, and clues are scattered across metrology, tool logs, chemical analyses, and facility systems, while increasing volumes make traditional dashboards slower and more fragmented.