在半导体生产中检测重要的特征并预测SEM检测到的缺陷的收益率
Umberto Amato1, Anestis Antoniadis1, Italia De Feis2
1Istituto di Scienze Applicate e Sistemi Intelligenti, National Research Council of Italy, 80131 Napoli, Italy.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
优化半导体制造涉及预测晶圆缺陷的最终产量. 本研究确定了关键的检查层,并开发了一个梯度增强模型来预测电气故障,提高半导体测试效率.
科学领域:
- 半导体制造业 半导体制造业
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
背景情况:
- 优化半导体生产需要基于过程中缺陷检测的准确产量预测.
- 扫描电子显微镜 (SEM) 对于在制造过程中识别晶圆缺陷至关重要.
研究的目的:
- 为扫描电子显微镜 (SEM) 检查确定最佳的半导体层.
- 利用检测到的缺陷,开发半导体电气故障的预测模型.
主要方法:
- 几率比率分析以根据其对最终产量的预测能力对检查层进行排名.
- 梯度提升回归/分类模型用于预测SEM检测到的缺陷的电气故障.
- 在两个独立的半导体数据集上验证两个模型.
主要成果:
- 确定了用于SEM检查的关键半导体层的排序列表,从而实现了集中的过程控制.
- 一个渐变增强模型成功预测了晶圆缺陷引起的电故障,证实了几率比率的发现.
- 两种开发的模型都有效地处理了数据缺陷,提高了预测准确度.
结论:
- 针对已识别的关键层进行有针对性的SEM检查,显著改善了半导体产量预测.
- 开发的渐变增强模型提供了一个强大的方法来预测半导体故障,优化生产过程.
- 这项研究为加强半导体质量控制和降低制造成本提供了可操作的见解.
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