Machine Learning and Polynomial Chaos models for Accurate Prediction of SET Pulse Current

This research investigates how heavy-ion irradiation affects the single event transient (SET) response of 14nm silicon-on-insulator (SOI) FinFET. The researchers generally use a TCAD tool (e.g., Sentauras TCAD) for developing a SET pulse current model. However, the TCAD simulations are time-consuming, which prohibits efficient design-space exploration. We propose efficient models for predicting SET pulse current with high accuracy. We use (1) polynomial chaos (PC) based models (2) ML regression techniques (3) artificial neural networks and 1Dconvolution neural network based models. Striking of a heavy-ion leads to transient behavior, which is very different from the normal behavior. Hence, for all the above predictors, we also evaluate the corresponding piecewise predictors. While TCAD tools take 4 hours for each simulation on a high-end computer, our proposed models take much lower latency (e.g., few seconds). This allows designers to explore a larger design space. Our proposed piecewise 1D-CNN model achieves state-of-the-art MSE which is 2.15× 1$0^{-6}$ mA-squared. Overall, our study provides insights into how PC and ML-based regression models can be used to enhance the efficiency of SET analysis in circuit design.