Intelligent Fab
Using AI and automation to make semiconductor manufacturing faster and smarter.
Overview
Advances in AI are making it possible for semiconductor manufacturers to automate, optimize, and innovate in new ways. At IBM, we’re developing new data and AI tools to help semiconductor manufacturers harness the power of AI for competitive edge across planning, running, and streamlining fab operations.
Our researchers are developing new methods for collecting and aggregating rich data sets from the fab including tools, equipment measurement, and sensor data. We are also creating AI applications that can be used for simulation and decision optimization, analytics and business insights, talent enablement and sustainable operations. These new tools will leverage our industry leading Manufacturing Execution System, SiView, which integrates with enterprise systems to facilitate tighter supply chain and manufacturing linkages, and respond more quickly to market changes.
Our work
- ExplainerPeter Hess
- Why we need EUV lithography for the future of chipsExplainerMike Murphy
- The path to 1 nanometer chips and beyondResearchMike Murphy
- The future of computer chips is being built in AlbanyDeep DiveMike Murphy
- VTFET: The revolutionary new chip architecture that could keep Moore’s Law alive for years to comeNewsBrent Anderson and Hemanth Jagannathan5 minute read
- Introducing the world's first 2 nm node chipNewsJulien Frougier and Dechao Guo5 minute read
Publications
- Design Optimization of ASIC Designs via AI-driven RTL-to-GDS Optimization with Floorplanning- Jenn Kazda
- Wachirawit Ponghiran
- et al.
 
- 2025
- DAC 2025
 
- Sequence-Aware Inline Measurement Attribution for Good-Bad Wafer Diagnosis- Kohei Miyaguchi
- Masao Joko
- et al.
 
- 2025
- ASMC 2025
 
- Wafer Defect Root Cause Analysis with Partial Trajectory Regression- Kohei Miyaguchi
- Masao Joko
- et al.
 
- 2025
- ASMC 2025
 
- Multimodal foundation models for more reproducible scientific experimentation and data capture- Patrick Ruch
- Gianmarco Gabrieli
- et al.
 
- 2024
- Future Labs Live 2024
 
- Cloud bursting an EDA workload with ML-driven technique for future SoC development- Wachirawit Ponghiran
- Jinwook Jung
 
- 2024
- VLSI Technology and Circuits 2024
 
- Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes- Dongxia Wu
- Ide-San Ide
- et al.
 
- 2024
- AISTATS 2024