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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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使用机器学习和光学造型测量来检测降低故障电压的故障.

James C Gallagher1, Michael A Mastro2, Alan G Jacobs2

  • 1U.S. Naval Research Laboratory, 4555 Overlook Ave SW, Washington, DC, 20375, USA. james.gallagher@nrl.navy.mil.

Scientific reports
|March 29, 2024
PubMed
概括
此摘要是机器生成的。

机器学习使用光学分析数据预测半导体晶圆的性能. 这种方法可以识别可能发生故障的化 (GaN) 设备,从而提高集成电路质量.

关键词:
在 GaN GaN 中.IIIV 半导体 半导体机器学习 机器学习通过光学造型测量,可以测量光学造型.垂直二极管是指垂直的二极管.

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科学领域:

  • 材料科学与工程:专注于半导体晶圆的制造和表征.
  • 电气工程:在高压和高频电源设备中的应用.

背景情况:

  • 半导体晶片制造需要精确控制集成电路 (IC) 质量的性能指标.
  • 化 (GaN) 为高压/高频电源设备提供了优势,但基板缺陷限制了性能.
  • 优化垂直GaN设备对于下一代动力电子设备至关重要.

研究的目的:

  • 应用机器学习 (ML) 来预测晶圆性能指标,特别是故障电压 (Vbk).
  • 用光学分析数据作为ML模型的输入.
  • 识别具有满足关键性能标准的高概率晶圆.

主要方法:

  • 使用光学造型测量来捕获晶圆表面特征的数据采集.
  • 实施机器学习算法来分析分析数据的数据.
  • 预测性能指标与实际设备故障电压 (Vbk) 的相关性.

主要成果:

  • 机器学习模型成功地预测了晶圆满足性能指标的概率.
  • 该方法可靠地识别出容易过早故障的设备 (低故障电压).
  • 光学造型测量数据证明在预测关键性能参数方面是有效的.

结论:

  • 机器学习与光学分析相结合,为半导体晶圆质量控制提供了一个强大的工具.
  • 这种预测能力可以减少故障设备的数量并提高制造产量.
  • 对于在中间故障电压下出现故障的设备,可能需要使用替代方法进行进一步的调查.