Simultaneous classification and out-of-distribution detection for wafer bin maps

Choi, Jeongman; Ma, Eun-Yeol; Kim, Heeyoung

doi:10.1080/08982112.2023.2286497

Quality Engineering

2023

DOI: 10.1080/08982112.2023.2286497

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Simultaneous classification and out-of-distribution detection for wafer bin maps

Jeongman Choi,

Eun-Yeol Ma,

Heeyoung Kim

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2024

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Cited by 2 publications

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Classification of Chip-Level Defect Types in Wafer Bin Maps Using Only Wafer-Level Labels

Lee,

Kim,

Kim

2024

Journal of Manufacturing Science and Engineering

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Defective chips in wafer bin maps (WBMs) form different spatial patterns depending on the root causes of process failures. Therefore, the identification of defect patterns in WBMs can help practitioners identify the root causes. Previous studies have focused on wafer-level classification even though chip-level classification can provide additional information regarding defect locations and defect sizes. Chip-level classification is more challenging than wafer-level classification because existing chip-level classification methods require chip-level labels, which are laborious to collect. We propose a method for chip-level defect classification using only wafer-level labels based on weakly supervised semantic segmentation. We first train a classification network using wafer-level labels and extract class activation maps (CAMs), which are visualizations of the discriminative regions. We then generate chip-level pseudo-labels using the extracted CAMs and use these labels to train a segmentation network, which predicts chip-level defect types. Experimental results verify effectiveness of the proposed method.

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Classification of Chip-Level Defect Types in Wafer Bin Maps Using Only Wafer-Level Labels

Lee,

Kim,

Kim

2024

Journal of Manufacturing Science and Engineering

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Few-Shot Classification of Wafer Bin Maps Using Transfer Learning and Ensemble Learning

Kim,

Yoon,

Kim

2024

Journal of Manufacturing Science and Engineering

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The high cost of collecting and annotating wafer bin maps (WBMs) necessitates few-shot WBM classification, i.e., classifying WBM defect patterns using a limited number of WBMs. Existing few-shot WBM classification algorithms mainly utilize meta learning methods that leverage knowledge learned in several episodes. However, meta-learning methods require a large amount of additional real WBMs, which can be unrealistic. To help train a network with a few real WBMs while avoiding this challenge, we propose the use of simulated WBMs to pre-train a classification model. Specifically, we employ transfer learning by pre-training a classification network with sufficient amounts of simulated WBMs and then fine-tuning it with a few real WBMs. We further employ ensemble learning to overcome the overfitting problem in transfer learning by fine-tuning multiple sets of classification layers of the network. A series of experiments on a real dataset demonstrate that our model outperforms the meta-learning methods that are widely used in few-shot WBM classification. Additionally, we empirically verify that transfer and ensemble learning, the two most important yet simple components of our model, reduce the prediction bias and variance in few-shot scenarios without a significant increase in training time.

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Simultaneous classification and out-of-distribution detection for wafer bin maps

Cited by 2 publications

References 30 publications

Classification of Chip-Level Defect Types in Wafer Bin Maps Using Only Wafer-Level Labels

Classification of Chip-Level Defect Types in Wafer Bin Maps Using Only Wafer-Level Labels

Few-Shot Classification of Wafer Bin Maps Using Transfer Learning and Ensemble Learning

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