Magnifying the Engineer: Where AI Fits in Semiconductor Analytics

By BRAD HOPPER, VP of Vertical Markets, Spotfire Three years into the generative AI boom, the constraint on the semiconductor industry is no longer demand — it's manufacturing capacity. In this Q&A, Brad Hopper, VP of Vertical Markets at Spotfire, explains how fabs, foundries, and equipment makers are turning to analytics and AI to squeeze more yield, quality, and throughput out of existing capacity rather than waiting for new capacity to come online. Hopper walks through the three engineering roles at the heart of any fab — process, product, and yield engineers — and the data-bridging problem each one faces when specialized tools (EDA, defect management, metrology, lithography overlay) don't talk to each other. Spotfire's approach, he says, is to blend AI into the analysis process to magnify engineers' expertise rather than replace it, citing real examples: an analog device maker tracing a wafer-position anomaly to a missed preventive-maintenance interval, and a fabless memory company cutting foundry validation cycles from months to weeks by unifying test data across three technologies. The conversation closes on what separates AI-assisted analysis from AI alone: live, interactive visualizations instead of static graphs; industry-native, certified computation instead of ad hoc code; and reusable methods that build institutional knowledge instead of one-off answers. Hopper's bottom line — the engineer still decides which thread to pull, but AI dramatically shortens the search. Click here to read the full article in Semiconductor Digest magazine.