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Biological Effects of Radiation02:59

Biological Effects of Radiation

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All radioactive nuclides emit high-energy particles or electromagnetic waves. When this radiation encounters living cells, it can cause heating, break chemical bonds, or ionize molecules. The most serious biological damage results when these radioactive emissions fragment or ionize molecules. For example, α and β particles emitted from nuclear decay reactions possess much higher energies than ordinary chemical bond energies. When these particles strike and penetrate matter, they...
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一种机器学习方法来预测微电子元件中的辐射效应.

Fernando Morilla1, Jesús Vega2, Sebastián Dormido-Canto1

  • 1Departamento de Informática y Automática, UNED, Juan del Rosal 16, 28040 Madrid, Spain.

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概括
此摘要是机器生成的。

本研究介绍了一种使用机器学习的先进电气参数预测器 (APEP),用于预测电子元件因辐射而降解的情况. 该APEP方法准确地预测了基于辐射剂量的组件性能变化,适用于各种电子部件.

关键词:
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科学领域:

  • 材料科学 材料科学 材料科学
  • 电气工程 电气工程
  • 计算机科学 计算机科学

背景情况:

  • 电子元件在暴露于辐射时容易降解.
  • 了解和预测这种退化对于辐射环境中的可靠性至关重要.
  • 现有的方法可能无法完全捕捉辐射剂量的复杂参数变化.

研究的目的:

  • 引入一种基于机器学习的新技术,用于预测电子元件因辐射而降解的情况.
  • 为了证明电气参数先进预测器 (APEP) 对双极晶体管的适用性.
  • 建立一个可通用的方法来预测各种电子元件的辐射诱导退化.

主要方法:

  • 电气参数高级预测器 (APEP) 技术采用机器学习算法.
  • 它涉及两个关键步骤:识别数据库中的降解模式,并预测新的,未经辐射的样本的降解.
  • 可以使用"纯数据驱动"或"基于模型"的方法来实施APEP.

主要成果:

  • APEP技术成功地预测了双极晶体管在辐射下电气参数的降解.
  • 该方法有效地模拟了电气参数如何随着辐射剂量的增加而变化.
  • 预测准确度证明了机器学习方法的可行性.

结论:

  • 电气参数先进预测器 (APEP) 提供了一种创新且有效的方法,用于预测电子元件的辐射诱导退化.
  • 该方法是可适应的,可以应用超越双极晶体管到其他电子元件.
  • 这种技术提高了在暴露于辐射的应用中对电子产品的可靠性评估.