{"title":"Few-shot Classification of Wafer Bin Maps Using Transfer Learning and Ensemble Learning","authors":"Hyeonwoo Kim, Heegeon Yoon, Heeyoung Kim","doi":"10.1115/1.4065255","DOIUrl":null,"url":null,"abstract":"\n 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.","PeriodicalId":507815,"journal":{"name":"Journal of Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4065255","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.