AI Semiconductor Value Chain
Design & IP
Chip architecture and intellectual property licensing. Companies like ARM, Synopsys, and Cadence provide the foundational designs and tools that define how semiconductors function.
EDA Tools
Electronic Design Automation software used to design and verify complex chip layouts before manufacturing. Essential for translating designs into manufacturable blueprints.
Lithography
Advanced machinery that uses extreme ultraviolet (EUV) light to print microscopic circuit patterns onto silicon wafers. ASML is the sole provider of cutting-edge EUV systems.
Other Fab Tools
Manufacturing equipment for etching, deposition, cleaning, and testing during chip production. Includes tools from Applied Materials, Lam Research, and Tokyo Electron.
Foundry (Leading Edge)
Semiconductor fabrication plants that manufacture the most advanced chips. TSMC dominates this space, producing cutting-edge processors for AI applications at 3nm and below.
Memory (HBM)
High Bandwidth Memory that enables rapid data transfer for AI training and inference. SK Hynix and Samsung lead in HBM production, critical for GPU performance.
Advanced Packaging
Technology to assemble and connect multiple chips into a single package, enabling higher performance and efficiency. Includes 3D stacking and chiplet integration.
Cloud Compute
Data centers and cloud infrastructure that deploy AI chips at scale. AWS, Microsoft Azure, and Google Cloud dominate, providing the computational backbone for AI services.
Software Ecosystems
Programming frameworks and libraries that enable developers to build AI applications. CUDA, PyTorch, and TensorFlow create the software layer that makes hardware accessible.
Understanding the AI Value Chain
The AI semiconductor value chain represents the critical layers of technology and manufacturing required to produce the advanced chips that power artificial intelligence.
Each layer represents a distinct stage in the process, from initial chip design through to deployment in cloud infrastructure.