  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.
