Using Graph Attention for Virtual Metrology in Semiconductor Manufacturing (Intel Foundry, ASU)
Researchers from Arizona State University and Intel Foundry have published “Graph Attention-Based Virtual Metrology for Film Deposition Processes in Semiconductor Manufacturing”. Abstract “Artificial intelligence-driven semiconductor manufacturing increasingly operates at nanometer and angstrom scales, where precise process control depends on accurate and timely metrology. However, physical metrology is limited by measurement latency, cost, and sampling constraints,... » read more The post Using Graph Attention for Virtual Metrology in Semiconductor Manufacturing (Intel Foundry, ASU) appeared
The increasing complexity and nanometer-scale operations in semiconductor manufacturing necessitate advanced, AI-driven metrology solutions to overcome limitations of traditional physical measurement techniques.
This development indicates a tangible step towards more autonomous and efficient chip production, directly impacting the cost, quality, and speed of semiconductor supply chains.
The adoption of AI, specifically graph attention, for virtual metrology will reduce measurement latency and improve process control, potentially accelerating manufacturing cycles and reducing defects.
- · Intel Foundry
- · Arizona State University
- · Semiconductor equipment manufacturers
- · AI/ML algorithm developers
- · Traditional physical metrology equipment manufacturers (if not adapting)
- · Semiconductor fabs reliant on manual process control
Improved semiconductor manufacturing efficiency and yield rates through faster, more accurate virtual metrology.
Reduced operational costs for fabs and accelerated iteration cycles for new chip designs due to enhanced process feedback.
Increased global competition in advanced chip manufacturing as AI-driven automation becomes a key differentiator, potentially leading to further consolidation or specialization.
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Read at Semiconductor Engineering