感知器
人工智能
支持向量机
计算机科学
瞬态(计算机编程)
集合(抽象数据类型)
事件(粒子物理)
线性回归
决策树
人工神经网络
脉搏(音乐)
算法
多层感知器
机器学习
物理
探测器
量子力学
程序设计语言
操作系统
电信
作者
Marko Andjelković,Junchao Chen,Rizwan Tariq Syed,Miloš Marjanović,Goran Ristić,Miloš Krstić
标识
DOI:10.1109/miel58498.2023.10315809
摘要
In this work, the use of artificial intelligence (AI) algorithms for the prediction of generated Single Event Transient (SET) pulse width in standard combinational cells is analyzed. The SET generation is characterized using the current injection approach in Spectre simulations. Based on conducted simulations, the dataset of SET pulse width in terms of gate driving strength, supply voltage, temperature and particle Linear Energy Transfer (LET) is created. The dataset is then used to train six standard machine learning models: linear regression, polynomial regression, support vector machine, decision tree, random forest, and multi-layer perceptron. For all models, the largest prediction error was observed for small SET pulse widths (< 100 ps). The multilayer perceptron model has shown the best accuracy in SET pulse width prediction, with the correlation coefficient R 2 > 0.99.
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