发表机构
Infineon Technologies Sdn. Bhd.（英飞凌科技（马来西亚）有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究半导体行业双重转型问题，通过对1465篇文献范围审查，揭示知识结构漏洞，提出基于系统之系统范式的6层设计安全与可持续性架构，建立整合路径，展示相关机制构建可溯源数据结构，推动监管合规与创新。
AI 中文摘要
半导体行业面临双重转型：通过人工智能扩大制造执行规模，同时满足严格的可持续性要求，如欧盟碳边境调整机制。本文对1465篇文献进行范围审查，涵盖人工智能集成计量、供应链环境、社会及治理和联邦工业数据空间。网络分析揭示高度分散的“核心-外围”知识结构，强调人工智能驱动的过程优化与下游可持续性治理间的关键结构漏洞。为填补这些差距，本研究提出基于系统之系统范式的6层设计安全与可持续性架构。通过建立不同的“网格到核心”和“标准贯穿供应链”整合路径，展示虚拟计量、局部联邦学习和防御性监管科技机制如何构建可溯源数据结构。最终，该架构将监管合规定位为创新驱动力，实现半导体制造中的安全、气候中和及循环价值链。
英文摘要
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.
Commentsto be published in the 32nd IEEE ICE/ITMC Conference (Porto, Portugal) proceedings
