Abstract As transistor technologies continue to shrink to advanced nodes like 7nm, 5nm, and beyond, designers face increasing challenges due to various nonideal effects. Issues such as subthreshold leakage, gate leakage, parasitic capacitance, mobility degradation, and variations from the manufacturing process make it difficult to accurately predict and optimize power in MOSFETbased circuits. In this study, we focus on a specific type of multigate MOSFETarchitecture, such as FinFET or GAA-FET. The devices were calibrated using TCAD simulations along with physical validation to ensure accuracy. Standard power estimation methods often fail to account for non-ideal effects, limiting their accuracy at advanced nodes. This work proposes a compact hybrid machine learning model that improves power estimation by including these effects. With a hybrid DRC framework (Deep Neural Network, Random Forest, CatBoost), the model was trained on simulation data generated by TCAD and executed within Visual Studio. To evaluate performance, the proposed hybrid model was created based on four other models, RF, DNN, A Neural Network (ANN), and a hybrid XGBoost model. The experimental data showed that the proposed hybrid had a maximum prediction accuracy of 96.31 %, which is a significant improvement over traditional methods. This level of performance demonstrates the capability of hybrid ML models to improve power estimation in the VLSI design flow, which can shorten development cycles and improve design dependability. The Random Forest algorithm was selected for its robustness to overfitting and capacity tohandle high-dimensional datasets. The combined architecture allows model generalization and interpretability.
Teja et al. (Tue,) studied this question.
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