Institution profile

Infineon Technologies AG

Industry researcheurope · de
Official website
Research library16linked papers
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Selected work

Representative Papers

Physics-Informed Condition Monitoring of SiC Power Modules

Aug 08, 2026

This study addresses the lack of efficient health monitoring methods for silicon carbide (SiC) power modules in embedded applications, where physics-based models are difficult to deploy online and purely data-driven approaches suffer from poor generalization and heavy reliance on large labeled datasets. To overcome these limitations, this work proposes a lightweight hybrid framework that integrates physical priors with data-driven learning. The approach incorporates cumulative damage features derived from Miner’s rule, enforces monotonic degradation through gradient penalty constraints, and models outputs using a heavy-tailed distribution to enhance robustness against anomalous disturbances—such as wire bond lift-off—and improve uncertainty calibration. Evaluated on Infineon’s industrial power cycling dataset, the method reduces mean absolute error by approximately 70% compared to purely data-driven baselines, demonstrates stable performance under rigorous cross-validation, and meets the computational constraints required for embedded deployment.

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Failure-Mechanism Transferability of Cumulative-Damage Features for Health State Estimation of SiC Power Modules

Aug 08, 2026

Existing data-driven approaches struggle to generalize across different failure mechanisms in state-of-health estimation for silicon carbide power modules. To address this limitation, this work proposes a physics-informed neural ordinary differential equation (NODE) model that integrates cumulative electrothermal features, specifically targeting two representative failure modes: solder layer fatigue and bond wire lift-off. The model’s transferability is rigorously evaluated through multi-mechanism accelerated aging experiments and k-fold cross-validation. Results demonstrate that the choice of input feature representation influences transfer performance as significantly as model architecture. The proposed approach, leveraging cumulative damage-based features, maintains consistent accuracy across both failure mechanisms, with error variations confined within inter-fold variance bounds, substantially outperforming conventional methods and NODE variants using baseline features.

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

Jul 25, 2026

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.

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Recent publications

Latest Papers

Physics-Informed Condition Monitoring of SiC Power Modules

Aug 08, 2026

This study addresses the lack of efficient health monitoring methods for silicon carbide (SiC) power modules in embedded applications, where physics-based models are difficult to deploy online and purely data-driven approaches suffer from poor generalization and heavy reliance on large labeled datasets. To overcome these limitations, this work proposes a lightweight hybrid framework that integrates physical priors with data-driven learning. The approach incorporates cumulative damage features derived from Miner’s rule, enforces monotonic degradation through gradient penalty constraints, and models outputs using a heavy-tailed distribution to enhance robustness against anomalous disturbances—such as wire bond lift-off—and improve uncertainty calibration. Evaluated on Infineon’s industrial power cycling dataset, the method reduces mean absolute error by approximately 70% compared to purely data-driven baselines, demonstrates stable performance under rigorous cross-validation, and meets the computational constraints required for embedded deployment.

0 citationsRead paper

Failure-Mechanism Transferability of Cumulative-Damage Features for Health State Estimation of SiC Power Modules

Aug 08, 2026

Existing data-driven approaches struggle to generalize across different failure mechanisms in state-of-health estimation for silicon carbide power modules. To address this limitation, this work proposes a physics-informed neural ordinary differential equation (NODE) model that integrates cumulative electrothermal features, specifically targeting two representative failure modes: solder layer fatigue and bond wire lift-off. The model’s transferability is rigorously evaluated through multi-mechanism accelerated aging experiments and k-fold cross-validation. Results demonstrate that the choice of input feature representation influences transfer performance as significantly as model architecture. The proposed approach, leveraging cumulative damage-based features, maintains consistent accuracy across both failure mechanisms, with error variations confined within inter-fold variance bounds, substantially outperforming conventional methods and NODE variants using baseline features.

0 citationsRead paper

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

Jul 25, 2026

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.

0 citationsRead paper