A wafer bin map (WBM) is a spatial map of binary values for individual chips on a wafer, representing the wafer testing results for each chip. Different patterns of defective chips in WBMs are related to different root causes of process failures, and thus the classification of defect patterns in WBMs helps to detect the process failures and identify their causes. In recent studies, convolutional neural networks (CNNs) have shown effective performance on the classification of the defect patterns in WBMs owing to their high expressive power. However, previous studies implicitly assumed that the labels of WBMs used for training the CNNs are correct, although the labels can be often incorrect due to annotation errors. The possibility of such mislabeling increases when WBMs have mixed-type defect patterns. However, when trained on mislabeled data, CNNs with the standard cross entropy loss can easily overfit to mislabeled samples, which leads to poor generalization on test data. To overcome this issue, we propose a novel training algorithm called \textit{sample bootstrapping}. Sample bootstrapping identifies which samples have clean or noisy labels using a two-component beta mixture model (BMM) and measures the uncertainty of each identified label (clean or noisy) using the posterior probability of the component assignment. Then, only the samples with low uncertainty of the estimated labels are selected to compose mini-batches via weighted random sampling, where the sampling weights are determined based on the posterior probability calculated from the BNN. Finally, the CNNs are trained on these mini-batches with the dynamic bootstrapping loss, a recent modification of the cross entropy loss to account for label noise. In this way, we can correct only the samples that are highly likely to have noisy labels, and prevent the risk of false correction of actually true-labeled samples. Our experiments on simulated WBM datasets under various noise levels and noise types demonstrate better test accuracy of sample bootstrapping than other competing methods.
웨이퍼 빈 맵 (WBM)은 각 칩에 대한 불량 여부 테스트 결과에 따라 이진 값으로 표현된 맵을 말한다. 웨이퍼 빈 맵에서 나타나는 다양한 결함 패턴은 공정 결함의 원인과 밀접한 관련이 있기 때문에 많은 연구가 웨이퍼 빈 맵의 결함 패턴을 분류하는 데 중점을 두고 있다. 그러나, 최근 웨이퍼 빈 맵 결함 패턴의 분류 연구들에서 학습에 사용된 라벨이 정확하다고 가정해왔지만, 주석 오류로 인해 라벨이 종종 부정확할 수 있다. 특히, 웨이퍼 빈 맵에 혼합형 결함 패턴이 있는 경우, 이렇게 라벨링이 잘 못될 가능성이 높아진다. 본 논문에서는 라벨 노이즈로 혼합형 결함 패턴을 분류하는 새로운 방법론을 제안한다. 구체적으로는 베타 혼합 모델 (BMM)을 사용하여 각 라벨의 노이즈 여부를 확인하고, 사후 확률을 사용하여 신뢰도가 높은 샘플만 선택적으로 학습하도록 하였다. 실험을 통하여, 제안 모델이 다른 경쟁 방법보다 더 나은 분류 성능을 보였다.
