Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Abstract

Single-event effects (SEE) are an important index of radiation resistance for fully depleted silicon on insulator (FDSOI) devices. The research into traditional FDSOI devices is based on simulation software, which is time consuming, requires a large amount of calculation, and has complex operations. In this paper, a prediction method for the SEE of FDSOI devices based on deep learning is proposed. The characterization parameters of SEE can be obtained quickly and accurately by inputting different particle incident conditions. The goodness of fit of the network curve for predicting drain transient current pulses can reach 0.996, and the accuracy of predicting the peak value of drain transient current and total collected charge can reach 94.00% and 96.95%, respectively. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.10 × 102 and 1.38 × 103 times, respectively. This method can significantly reduce the computational cost, improve the simulation speed, and provide a new feasible method for the study of the single-event effect in FDSOI devices.

A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks

“…This is accomplished by subtracting the batch mean from each input data point and dividing it by the batch standard deviation. The batch means and standard deviation are estimated using the input data of a batch rather than the entire data set [33]. Batch normalization helps to reduce the problem of internal covariate drift, which occurs when there is high variation in the input data.…”