Scoping Review Links AI, Metrology, and ESG in Semiconductor Sector
A recent scoping review featured on arXiv (2607.23082) explores the convergence of artificial intelligence, metrology, and environmental, social, and governance (ESG) considerations within the semiconductor sector. The research evaluates 1,465 documents sourced from Web of Science and Scopus, focusing on AI-enhanced metrology, ESG in supply chains, and federated industrial data environments. Through network analysis, a fragmented 'core-periphery' knowledge framework is identified, revealing a significant disconnect between AI-led process optimization and sustainability governance. To bridge this gap, the authors suggest a six-layer Safe and Sustainable by Design (SSbD) architecture rooted in a System of Systems (SoS) approach, incorporating 'grid-to-core' and 'standards-through-supply-chain' integration methods, while addressing the challenge of scaling manufacturing in line with sustainability regulations such as the EU Carbon Border Adjustment Mechanism (CBAM).
Key facts
- Review covers 1,465 documents from Web of Science and Scopus.
- Focus on AI-integrated metrology, supply chain ESG, and federated industrial data spaces.
- Network analysis shows fragmented core-periphery knowledge structure.
- Identifies structural hole between AI-driven process optimization and sustainability governance.
- Proposes 6-layer Safe and Sustainable by Design (SSbD) architecture.
- Architecture based on System of Systems (SoS) paradigm.
- Introduces grid-to-core and standards-through-supply-chain integration pathways.
- Uses virtual metrology (VM) and localized federated learning.
- Context includes EU Carbon Border Adjustment Mechanism (CBAM).
Entities
Institutions
- Web of Science
- Scopus
- arXiv
- European Union