AI Digital Twins Race to Boost Chip Yields

Lam’s Fabtex™ Yield Optimizer uses AI-powered digital twins to speed yield ramp and cut chip production costs

13 Feb 2026

Lam Research has launched a new software platform that brings artificial intelligence and digital twin technology deeper into semiconductor manufacturing, as chipmakers seek to improve yields without raising costs.

The US equipment group introduced Fabtex™ Yield Optimizer under its Semiverse® Solutions suite. The system combines AI models with digital twins, virtual replicas of fabrication processes, allowing engineers to test process changes in simulation before applying them to physical wafers.

In advanced chip production, even minor variations can have large financial effects. As manufacturers shift to smaller process nodes, tolerances narrow and production steps multiply. Improving yield has traditionally required repeated cycles of running wafers, inspecting defects and adjusting settings on the fab floor, a method that is precise but costly in time and materials.

Fabtex™ is designed to move part of that work into a digital environment. Engineers can assess multiple variables at once in a high-fidelity simulation, rather than altering them sequentially in live production. Lam said the approach can help identify yield risks earlier and support faster production ramps while reducing waste.

The economic rationale is clear. Advanced fabrication tools and mask sets can each cost tens of millions of dollars. Small efficiency gains, when applied across high-volume lines, can have a significant effect on margins.

Lam’s move reflects a broader shift in the industry. Chipmakers and equipment suppliers are investing in AI, machine learning and virtualisation to improve defect detection, strengthen process control and enhance predictive maintenance. Digital twins are becoming a central part of these efforts, offering a systems-level view of complex production flows.

Implementation, however, presents challenges. Integrating AI-driven platforms into existing facilities requires secure data infrastructure, compatibility with older systems and safeguards for proprietary information. Companies must also adapt internal workflows to make use of simulation-based insights.

As chip architectures grow more intricate and capital spending remains high, manufacturers are under pressure to extract more output from each line. Tools that promise earlier insight into yield risks may become an increasingly important part of that strategy.