Scoping Review of AI, Metrology, and ESG in the Semiconductor Sector: Implications for Safe and Sustainable by Design (SSbD)

📅 2026-07-25
📈 Citations: 0
Influential: 0
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🤖 AI Summary
This study addresses the dual challenge faced by semiconductor manufacturing: leveraging AI to enhance operational efficiency while complying with emerging sustainability regulations such as the EU’s Carbon Border Adjustment Mechanism (CBAM). Recognizing a structural disconnect between AI-driven process optimization and sustainability governance, the work synthesizes insights from 1,465 scholarly articles to propose a novel six-layer “Safe and Sustainable by Design” (SSbD) architecture grounded in a system-of-systems approach. Integrating virtual metrology, localized federated learning, and defensive regulatory technology, this framework bridges critical knowledge gaps across the entire value chain—from grid to chip—and aligns with global supply chain standards. It transforms regulatory compliance into an innovation catalyst, enabling a traceable, secure, climate-neutral, and circular data value chain for the semiconductor industry.
📝 Abstract
The semiconductor sector faces a dual transition: scaling manufacturing execution through Artificial Intelligence (AI) while satisfying stringent sustainability mandates, such as the EU Carbon Border Adjustment Mechanism (CBAM). This paper presents a scoping review of 1,465 documents indexed in Web of Science and Scopus, spanning AI-integrated metrology, supply chain ESG, and federated industrial data spaces. Network analysis reveals a highly fragmented "core-periphery" knowledge structure, emphasizing a critical structural hole between AI-driven process optimization and downstream sustainability governance. To close these gaps, this study proposes a 6-layer Safe and Sustainable by Design (SSbD) architecture grounded in a System of Systems (SoS) paradigm. By establishing distinct "grid-to-core" and "standards-through-supply-chain" integration pathways, the proposed framework demonstrates how virtual metrology (VM), localized federated learning, and defensive RegTech mechanisms can build provenance-aware data fabrics. Ultimately, this architecture positions regulatory compliance as a driver for innovation, enabling secure, climate-neutral, and circular value chains in semiconductor manufacturing.
Problem

Research questions and friction points this paper is trying to address.

AI
Metrology
ESG
Semiconductor
Sustainability
Innovation

Methods, ideas, or system contributions that make the work stand out.

Safe and Sustainable by Design (SSbD)
System of Systems (SoS)
virtual metrology
federated learning
RegTech
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Karen Ang
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