Insights from the SEMICON West Session “Reimaging and Transforming Package Assembly and Test Manufacturing in the AI Era”
Across the Advanced Imaging track, speakers consistently described imaging’s transformation from a reactive inspection function into a proactive intelligence layer. As fabs embrace AI-driven manufacturing, imaging systems are becoming essential data sources for learning, prediction, and optimization across the factory. This evolution places imaging at the center of digital twins, closed-loop process control, and data-driven decision-making.
Imaging as a Continuous Manufacturing Sensor
Modern imaging platforms generate rich, high-dimensional datasets that extend far beyond defect counts. These datasets feed AI models that detect subtle process drift, predict yield excursions, and enable adaptive process tuning in near real time.
Several speakers noted that imaging data increasingly underpins digital twins capable of simulating entire process flows—allowing engineers to evaluate changes virtually before deploying them on the factory floor.
Turning Volume into Value
With increased imaging resolution comes exponential data growth. Sessions emphasized that the competitive advantage lies not in collecting more images, but in extracting actionable insight through intelligent analytics, feature extraction, and AI-assisted interpretation.
Why It Matters
- AI-enabled fabs depend on high-quality imaging data
- Imaging enables predictive, not just reactive, control
- Analytics maturity determines imaging’s real value
What to Watch
- AI-native imaging platforms
- Standardized imaging data architectures
- Expanded use of imaging-driven digital twins
Source: “Afternoon Session: Building and Scaling AI: From Design to Productization,” SEMICON West 2025. Speakers: Ravi Mahajan (Intel); William Chen (IEEE EPS); PR Chidambaram (Qualcomm); C.P. Hung (ASE); Tim Lee (Boeing & IEEE); Ming Li (Lam Research); Jeff Pettinato (Intel Corporation). Panel moderator: Audrey Charles (Lam Research). Panelists: C.P. Hung (ASE); Tim Lee (Boeing & IEEE); Jeff Pettinato (Intel Corporation); Ming Li (Lam Research); PR Chidambaram (Qualcomm).
