With high device integration density and evolving sophisticated device structures in semiconductor chips, detecting defects becomes elusive and complex. Conventionally, machine learning (ML)-guided failure analysis is performed with offline batch mode training. However, the occurrence of new types of failures or changes in the data distribution demands retraining the model. During the manufacturing process, detecting defects in a single-pass online fashion is more challenging and favoured. This paper focuses on novel quantile online learning for semiconductor failure analysis. The proposed method is applied to semiconductor device-level defects: FinFET bridge defect, GAA-FET bridge defect, GAA-FET dislocation defect, and a public database: SECOM. From the obtained results, we observed that the proposed method is able to perform better than the existing methods. Our proposed method achieved an overall accuracy of 86.66% and compared with the second-best existing method it improves 15.50% on the GAA-FET dislocation defect dataset.

翻译：随着半导体芯片中器件集成度不断提高以及器件结构日益复杂化，缺陷检测变得难以捉摸且复杂度显著提升。传统上，机器学习引导的失效分析采用离线批处理模式进行训练。然而，新型故障的出现或数据分布的变化要求对模型进行重新训练。在制造过程中，以单次在线方式检测缺陷更具挑战性且更受青睐。本文聚焦于新颖的分位数在线学习方法用于半导体失效分析。所提出的方法被应用于半导体器件级缺陷：FinFET桥接缺陷、GAA-FET桥接缺陷、GAA-FET位错缺陷，以及一个公共数据库：SECOM。从所获得的结果中，我们观察到所提方法能够优于现有方法。我们的方法实现了86.66%的整体准确率，与次优的现有方法相比，在GAA-FET位错缺陷数据集上提升了15.50%。