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
概括
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.
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.
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