Recurrent Neural Network Based Aging Open Model Interface for AI-based Compact Model

In this work, a Recurrent Neural Network-based Open Model Interface (RNN-OMI) is proposed to enable AI-driven compact model (CM) to characterize the typical aging behavior of MOSFETs, specifically Negative Bias Temperature Instability (NBTI). The long-term sequence learning ability of RNN assists in fine-tuning the sensitive parameters within AI-CM. The enhanced AI-CM predicts the drift of SRAM butterfly curves, enabling the temporal prediction in circuit aging evaluation.