How AI Based Design Changes Will Impact Manufacturing
Tuesday, October 13, 2026
10:30am–4:30pm PT
SESSION
The future of semiconductor design is shifting toward bespoke, highly customized devices produced in lower volumes—driven by domain-specific AI, chiplets, and application tailored architectures. This trend raises the bar for design agility, verification speed, and rapid iteration across the entire ecosystem.
• What does this mean for semiconductor manufacturing? As product mixes diversify and volumes fragment, manufacturing must become more flexible—supporting faster NPI, tighter design-to-manufacturing feedback, and resilient supply strategies. We’ll discuss how data-driven process optimization and closer collaboration between design houses, foundries, and systems companies can reduce cycle time and improve yield in this new reality.
• How does AI help? From requirements-to-architecture exploration and faster verification, to intelligent DFM, virtual metrology, and predictive maintenance, AI is becoming a practical lever for both design productivity and fab efficiency. Learn where AI is delivering measurable impact today, what data and infrastructure are required, and how to scale these approaches across the design and manufacturing lifecycle.
CTA: Explore how AI can enable bespoke design, shorten design-to-silicon cycles, and accelerate semiconductor manufacturing through smarter collaboration, automation, and data-driven decision-making.
Hear from Experts in:
• System: Perspectives from OEMs and system architects on end-application requirements, performance/power targets, and how software, packaging, and silicon co-optimization is shaping next-generation platforms.
• Fabless: Insights on rapid product definition, IP and chiplet strategy, verification at scale, and how teams are using AI-enabled flows to shorten schedules while maintaining quality and compliance.
• Foundry: What’s changing in PDK enablement, DFM and yield learning for increasingly diverse designs—plus practical approaches to improving design-to-manufacturing feedback loops with data and automation.