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基于新型TFET的单颗粒子辐射效应的算法预测

Chen Chong¹, Hongxia Liu¹, Shulong Wang¹

¹School of Microelectronic, Xidian University, Xi'an 710068, People's Republic of China.

|August 9, 2024

概括

一个深度学习模型准确地预测了道场效应晶体管 (TFET) 上单个粒子辐射的影响. 这种数据驱动的方法为了解辐射下的设备可靠性提供了可靠的工具,优于传统的机器学习方法.

科学领域:

半导体设备物理学半导体设备物理
电子产品中的辐射效应.
工程领域的人工智能

背景情况:

单粒子辐射对半导体设备的可靠性构成风险,例如道场效应晶体管 (TFET).
预测这种辐射引起的短暂影响对于确保设备性能和寿命至关重要.

研究的目的:

开发和验证一种深度学习算法,用于预测TFET中单颗粒子的短暂特性.
对传统的机器学习方法和TCAD模拟进行深度学习模型的性能评估.

主要方法:

构建了一个深度学习网络模型,以预测单粒子瞬态的关键参数.
用计算机辅助设计 (TCAD) 模拟来研究单颗粒子对堆叠源沟门TFET的影响.
深度学习模型的预测与五种传统的机器学习算法进行了比较.

主要成果:

增加的排水电压,重离子事件宽度和线性能量转移导致了更高的排水过渡电流和收集电荷.
深度学习模型实现了高准确度,排放电流脉冲峰值 (I_DMAX) 和收集电荷 (Q_c) 的相对误差百分比低于10%,大多数预测低于1%.
与支持矢量机器,决策树,K-近邻,回归和线性回归相比,深度学习算法表现出优异的性能和较低的平均误差.

结论:

开发的数据驱动深度学习模型有效地预测TFETs在各种条件下的单粒子过渡数据.
该模型是研究人员和数字电路设计人员评估辐射下设备可靠性的宝贵工具.
深度学习显示,在推进半导体设备中辐射效应的预测方面,有很大的前景.

关键词:

深度学习是一种深度学习.一个单颗粒子的过渡性.堆叠源沟门道FET (SSTGTFET) 的使用

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Field Effect Transistor

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