FACT: A New Framework for Aging-Aware Circuits Critical Path Prediction With Enhanced Adaptability

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Abstract
With the continuous scaling of integrated circuits, aging effects pose severe challenges to circuits’ timing analysis. Accurately predicting circuit delays after aging, thereby ensuring circuit reliability throughout its lifecycle, becomes increasingly critical. Identifying the critical path plays a crucial role in this procedure. This paper introduces FACT, a framework that integrates a GNN-based mixture of experts with a masked multi-modal attention mechanism to enhance adaptability. FACT dynamically assigns experts based on circuit inputs and leverages masked attention to capture and fuse multi-scale information across nodes, improving its generalization to unseen circuits. To the best of our knowledge, this framework is the first to achieve accurate prediction of cell delay while simultaneously predicting the critical path after aging. Experimental results show that FACT performs well in terms of accuracy, efficiency, interpretability, and transferability. The average prediction precision of cell delay reaches 93.25%, along with the average prediction accuracy of the critical path reaching 97.86%. Therefore, FACT provides a promising approach for the reliability-aware circuit design.
Funding Information
  • National Natural Science Foundation of China (T2293704)
  • National Key Research and Development Program of China (2023YFB4402204)
  • Beijing Outstanding Young Scientist Program (JWZQ20240101004)

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