Framework Proposed for Semiconductor Manufacturers to Meet EU Sustainability and Compliance Requirements
A preprint paper proposes a zero-trust, AI-agent-based governance architecture to help advanced semiconductor manufacturing facilities comply with converging EU sustainability and data regulations. The framework uses role-based autonomous agents operating inside hardware-isolated trust zones to generate cryptographically signed compliance reports without exposing proprietary manufacturing data. The work addresses a growing tension between regulatory transparency demands and corporate data privacy in the semiconductor industry.
The paper, submitted to arXiv and initially accepted for the 2026 IEEE ICE/ITMC Conference before withdrawal due to submission volume limits, responds to the simultaneous introduction of three major EU regulatory instruments: the Safe and Sustainable by Design (SSbD) framework, the Corporate Sustainability Due Diligence Directive (CSDDD), and the Carbon Border Adjustment Mechanism (CBAM). The authors argue these overlapping mandates have created a governance bottleneck that manual corporate reporting cannot handle. Their proposed solution centers on 'Professional Proxies'—autonomous, role-based AI agents assigned to Facility, Process Engineering, and Finance functions—that coordinate in a five-step relay workflow within a six-layer SSbD reference architecture. Critically, computationally sensitive tasks such as Virtual Metrology predictions and Federated Machine Learning are executed inside Trusted Execution Environments (TEEs), allowing fabs to share verifiable compliance tokens via International Data Spaces connectors without revealing proprietary process recipes. The authors frame this as resolving a 'Data Sovereignty Paradox' and position the framework as a pathway toward net-zero Industry 5.0 semiconductor ecosystems.
What's missing
The paper is a preprint that was withdrawn from its initial conference venue and has not yet undergone peer review at a journal, so its technical claims—including the feasibility and scalability of TEE-based federated learning in live fab environments—remain unvalidated by independent reviewers. No empirical results from real semiconductor facilities are presented; the framework appears to be conceptual and architectural rather than experimentally demonstrated. Open questions include latency and throughput costs of running ML workloads inside TEEs at production scale, and how the framework would handle cross-jurisdictional regulatory conflicts beyond the EU.
What different sources said
- arXiv cs.AICenter
Trustworthy Smart Fabs via Professional Proxies: Scaling Safe and Sustainable by Design (SSbD) through Industrial Data Spaces
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