What is Safe and Sustainable by Design (SSbD)?

Safe and Sustainable by Design (SSbD)

Driven by the European Union’s 2026 chemical and industrial strategy mandates, the Safe and Sustainable by Design (SSbD) framework establishes an upstream, proactive paradigm that integrates safety, circularity, and functional performance from the earliest stages of material and process engineering (Liao et al. 2026).

Rather than relying on retrospective, “end-of-pipe” pollution abatement strategies, SSbD shifts the compliance burden into early-stage product lifecycle design. This model systematically preempts industrial risk by evaluating material flows, environmental toxicities, and occupational health hazards across the complete value chain (Ang and Liao forthcoming, 2026).

SSbD Architecture in Smart Fabs

To deploy SSbD inside complex semiconductor environments, advanced production lines must transition from static reporting to real-time, multi-layered data architectures (Ang and Liao forthcoming, 2026).

  • Virtual Metrology (VM): Leverages AI models to run continuous, non-destructive monitoring of physical and environmental parameters on the factory floor (Ang and Liao forthcoming, 2026; Liao et al. 2026).
  • Socio-Technical Orchestration: Balances tool-level physical yields with stringent regulatory compliance matrices like the Corporate Sustainability Due Diligence Directive (CSDDD) and the Carbon Border Adjustment Mechanism (CBAM) (Liao et al. 2026).

Trust Verification via Industrial Data Spaces

A persistent barrier to executing SSbD within the semiconductor value chain is the Data Sovereignty Paradox: fabs must prove sustainability compliance without exposing proprietary process recipes (Liao et al. 2026).

  • Professional Proxies: Role-based autonomous agentic workflows coordinate an automated “relay race” between Facility, Process, and Finance engineering teams (Liao et al. 2026).
  • Trusted Execution Environments (TEEs): Computational logic runs within hardware-isolated trust zones, enabling Federated Machine Learning (FML) to calculate metrics securely (Liao et al. 2026).
  • Cryptographic Export: Generates cryptographically signed compliance tokens that are transmitted over International Data Spaces (IDS) connectors to verify upstream design integrity (Liao et al. 2026).

See Also: SEMI ESHS Integration

The technical execution of SSbD principles relies directly on operationalizing cross-industry frameworks, particularly linking macro-level design criteria with IRDS Roadmaps and SEMI standards (Liao and Ang forthcoming, 2026; Liao et al. 2026).

  • SEMI S23 & S2 (Safety & Energy): Quantifies early-stage tool modifications to accurately calculate tool-level Equivalent Energy Consumption Factors (Liao and Ang forthcoming, 2026).
  • SEMI MF & F Series (Metrology & Facilities): Standardizes real-time sensor streams to create trustworthy Digital Product Passports (DPPs), ensuring that clean, circular material flows feed back into the wafer fabrication lifecycle.

References

Ang, Karen, and Han-Teng Liao. forthcoming, 2026. “Scoping Review of AI, Metrology, and ESG in the Semiconductor Sector: Implications for Safe and Sustainable by Design (SSbD).” 2026 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC), forthcoming, 2026. https://doi.org/10.31235/osf.io/yknwt_v1.
Liao, Han-Teng, and Karen Ang. forthcoming, 2026. “From Stacks to Circuits: A Regenerative Socio-Technical Roadmap for AI Infrastructure Within Planetary Boundaries.” 2026 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC), forthcoming, 2026. https://doi.org/10.48550/arXiv.2606.10544.
Liao, Han-Teng, Chang-Yi Kao, and Karen Ang. 2026. Trustworthy Smart Fabs via Professional Proxies: Scaling Safe and Sustainable by Design (SSbD) Through Industrial Data Spaces. arXiv:2606.09227. arXiv. https://doi.org/10.48550/arXiv.2606.09227.