Hierarchical Mixture-of-Experts approach for neural compact modeling of MOSFETs

With scaling, physics-based analytical MOSFET compact models are becoming more complex. Parameter extraction based on measured or simulated data consumes a significant time in the compact model generation process. To tackle this problem, ANN-based approaches have shown promising performance improvements in terms of accuracy and speed. However, most previous studies used a multilayer perceptron (MLP) architecture which commonly requires a large number of parameters and train data to guarantee accuracy. In this article, we present a Mixture-of-Experts approach to neural compact modeling. It is 78.43% more parameter-efficient and achieves higher accuracy using fewer data when compared to a conventional neural compact modeling approach. It also uses 43.8% less time to train, thus, demonstrating its computational efficiency.