Algorithm prediction of single particle irradiation effect based on novel TFETs

Abstract

Abstract In order to predict the single particle irradiation of tunnel field effect transistor (TFET) devices, a deep learning algorithm network model was built to predict the key characterization parameters of the single particle transient. Computer aided design (TCAD) technique is used to study the influence of single particle effect on the novel stacked source trench gate TFET device. The results show that with the increase of drain voltage, incident width of heavy ions (less than 0.04 μm), and linear energy transfer, the drain transient current and collected charge also increase. The prediction results of deep learning algorithm show that the relative error percentage of drain current pulse peak (I DMAX) and collected charge (Q c) is less than 10%, and the relative error percentage of most predicted values is less than 1%. Comparison experiments with five traditional machine learning methods (support vector machine, decision tree, K-nearest algorithm, ridge regression, linear regression) show that the deep learning algorithm has the best performance and has the smallest average error percentage. This data-driven deep learning algorithm model not only enables researchers who are not familiar with semiconductor devices to quickly obtain the transient data of a single particle under any conditions; at the same time, it can be applied to digital circuit design as a data-driven device model reflecting the reliability of single particle transient. The application of deep learning in the field of device irradiation prediction has a highly promising prospect in the future.