Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning.
Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning.
复制标题
基于深度学习的FDSOI器件单粒子效应预测
DOI：
10.3390/mi14030502
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发表时间：
2023-02-21
期刊：
影响因子：
3.4
通讯作者：
Chen Y
中科院分区：
文献类型：
--
作者：
Zhao R;Wang S;Du S;Pan J;Ma L;Chen S;Liu H;Chen Y
单粒子效应（SEE）是全耗尽绝缘体上硅（FDSOI）器件抗辐照性能的重要指标。传统的FDSOI器件研究都是基于仿真软件进行的，耗时长，计算量大，操作复杂。提出了一种基于深度学习的FDSOI器件单粒子效应预测方法。通过输入不同的粒子入射条件，可以快速、准确地得到单粒子场的表征参数。该网络曲线对漏极瞬态电流脉冲的拟合优度可达0.996，对漏极瞬态电流峰值和总收集电荷的预测精度分别可达94.00%和96.95%。与TCAD Sentaurus软件相比，仿真速度分别提高了5.10 × 102和1.38 × 103倍。该方法可以显著降低计算成本，提高模拟速度，为FDSOI器件单粒子效应的研究提供了一种新的可行方法。
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.
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