Accelerating NBTI Aging Evaluation via Physics-Aware Graph Attention Networks
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866913172428947456 |
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| author | Zheng, RenJie Liu, HengXi Gao, ShuJun Li, Cong |
| author_facet | Zheng, RenJie Liu, HengXi Gao, ShuJun Li, Cong |
| contents | As semiconductor technology advances to smaller nodes, Negative Bias Temperature Instability (NBTI) under prolonged workloads has emerged as a significant bottleneck constraining reliability-aware Design-Technology Co-Optimization (DTCO). Conventional TCAD simulations incur prohibitive computational overhead when evaluating device aging characteristics, making it difficult to satisfy the demand for efficient iterative design cycles. To address this challenge, this paper proposes an aging evaluation framework based on a physics-aware graph attention network (Physics-Aware RelGAT). By losslessly mapping unstructured device meshes into attributed graphs, this framework constructs a 45-dimensional device encoding scheme that integrates interface trap distributions and macroscopic electro-thermal stresses, achieving a direct mapping from underlying physical quantities to device degradation characteristics. To overcome the challenge of predicting currents that span multiple orders of magnitude, a dual-end normalization strategy and a log-scale loss function optimization are introduced, ensuring the model possesses high-precision fitting capabilities. Experimental results demonstrate that the model achieves a mean error of only 1.27% on an independent test set, achieving an acceleration of approximately 17,000 times compared to traditional TCAD simulations. This framework provides a solution for the assessment of circuit reliability in advanced process nodes that successfully balances physical fidelity with industrial-grade efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31092 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accelerating NBTI Aging Evaluation via Physics-Aware Graph Attention Networks Zheng, RenJie Liu, HengXi Gao, ShuJun Li, Cong Emerging Technologies As semiconductor technology advances to smaller nodes, Negative Bias Temperature Instability (NBTI) under prolonged workloads has emerged as a significant bottleneck constraining reliability-aware Design-Technology Co-Optimization (DTCO). Conventional TCAD simulations incur prohibitive computational overhead when evaluating device aging characteristics, making it difficult to satisfy the demand for efficient iterative design cycles. To address this challenge, this paper proposes an aging evaluation framework based on a physics-aware graph attention network (Physics-Aware RelGAT). By losslessly mapping unstructured device meshes into attributed graphs, this framework constructs a 45-dimensional device encoding scheme that integrates interface trap distributions and macroscopic electro-thermal stresses, achieving a direct mapping from underlying physical quantities to device degradation characteristics. To overcome the challenge of predicting currents that span multiple orders of magnitude, a dual-end normalization strategy and a log-scale loss function optimization are introduced, ensuring the model possesses high-precision fitting capabilities. Experimental results demonstrate that the model achieves a mean error of only 1.27% on an independent test set, achieving an acceleration of approximately 17,000 times compared to traditional TCAD simulations. This framework provides a solution for the assessment of circuit reliability in advanced process nodes that successfully balances physical fidelity with industrial-grade efficiency. |
| title | Accelerating NBTI Aging Evaluation via Physics-Aware Graph Attention Networks |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2605.31092 |