AI Beyond the Pilot: Lessons from Deploying AI in Semiconductor Test

Speaker 

Patty Pun is a Principal Engineer and Engineering Manager in Qualcomm Technologies’ Product Development and Test Engineering (PDTE) organization.

Abstract

Generative AI has ignited widespread enthusiasm across the semiconductor industry, yet adopting it within semiconductor test environments presents distinct assurance, data-integrity, and operational challenges. In post-silicon validation and automated test engineering, the barrier to deployment is not simply model capability. Successful initiatives must also navigate fragmented specifications, complex proprietary test data, demanding quality requirements, and strict intellectual-property and data-access boundaries.

Drawing on real-world experience developing and deploying AI within a production semiconductor testing organization, this webinar examines the transition from experimental pilots to AI systems embedded within engineering workflows. The journey revealed a progression from information retrieval and connected data, to workflow-aware AI systems integrated into engineering processes, and ultimately toward coordinated engineering agents capable of supporting diagnosis, investigation, and cross-functional decision-making. Equally important were the organizational lessons learned around trust, adoption, governance, workflow integration, and engineering accountability.

A central observation emerged throughout this experience: AI helps you learn, but it does not learn for you. AI systems can rapidly synthesize test data, compare historical failures, generate hypotheses, and accelerate analysis. However, engineers remain responsible for judgment, intent, risk assessment, and high-assurance execution. Successful deployment therefore requires more than deploying advanced models. It requires establishing the data foundations, governance mechanisms, workflow integration, and organizational trust necessary to transform probabilistic AI capabilities into reliable engineering support.

Attendees will gain candid insights into adoption successes and failures, data-readiness bottlenecks, trust boundaries, and practical strategies for integrating AI into semiconductor engineering workflows. The webinar will conclude with a perspective on how engineering organizations may evolve from AI-assisted workflows toward coordinated human-AI teams and agent-enabled engineering operations.

Biography

Patty Pun is a Principal Engineer and Engineering Manager in Qualcomm Technologies’ Product Development and Test Engineering (PDTE) organization, where she leads AI knowledge and engineering intelligence initiatives. She holds B.S. and M.S. degrees in Electrical and Computer Engineering, with a minor in Robotics, from Carnegie Mellon University.

Over her career at Qualcomm, Patty has worked across wireless design optimization, semiconductor test, data platforms, and AI-enabled engineering systems. She led the development of a semiconductor test data ecosystem recognized with Qualcomm’s Distinguished Solution Recognition in 2024 and currently drives the development and adoption of AI platforms that enable knowledge sharing, workflow automation, and intelligent analytics across engineering organizations. Her recent work focuses on AI agents, engineering knowledge management, information governance, and responsible AI adoption in semiconductor development environments.