梳理1465篇文献，构建半导体产业可持续设计框架。
Scoping Review of AI, Metrology, and ESG in the Semiconductor Sector: Implications for Safe and Sustainable by Design (SSbD)
- 提出六层安全可持续设计架构，融合系统论思想
- 发现AI优化与可持续治理间存在关键知识断层
- 适合关注半导体可持续制造与合规创新的研究者
半导体行业面临双重转型：通过人工智能（AI）提升制造执行效率，同时满足欧盟碳边境调节机制（CBAM）等严格可持续性要求。本文对Web of Science和Scopus收录的1,465篇文献进行了范围综述，涵盖集成AI的计量技术、供应链ESG及联邦工业数据空间。网络分析揭示出高度碎片化的“核心-边缘”知识结构，凸显了AI驱动的过程优化与下游可持续性治理之间的关键结构性断层。为填补这些空白，本研究提出基于系统论（SoS）范式的六层安全可持续设计（SSbD）架构。通过建立‘从电网到核心’与‘标准贯穿供应链’两条整合路径，该框架展示了虚拟计量（VM）、本地化联邦学习及防御性RegTech机制如何构建具备溯源能力的数据底座。最终，该架构将监管合规转化为创新驱动力，推动实现安全、气候中立且循环的半导体制造价值链。
原文摘要 · Abstract (English)
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.
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