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In the semiconductor industry, the testing section has always played an important role. The testing section often requires engineers to judge the defect, which wastes a lot of time and cost. The accurate classification can provide useful information for engineers through neural networks. In this paper, we present a method for wafer map data augmentation and defect classification. Data augmentation is based on CNN encoder-decoder and the classification is based on depthwise separable convolutions. There are two datasets used, one is open dataset WM-811K and the other is built with a Taiwan company. We train two models with mobilenetV1 and V2 for the different datasets. The light-weight deep convolution can reduce model parameters and calculations, which is very efficient for the testing house with large production volumes. On two different data sets, our proposed method can reduce the number of parameters by 30% and 95%, and reduce the amount of calculation by 75% and 25%, respectively. The test accuracy of the first dataset is 93.95%. The second dataset test accuracy is 87.04%. After the data augmentation, accuracy is increased to 97.01% and 95.09%, respectively. [ABSTRACT FROM AUTHOR]

Manual titration of positive airway pressure (PAP) is a gold standard to provide an optimal pressure for the treatment of obstructive sleep apnea-hypopnea syndrome (OSAS). Since manual titration studies were costly and time-consuming, many statistical models for predicting effective PAPs were reported. However, the prediction accuracies of the models associated with nocturnal parameters still remain low. This study proposes a fuzzy neural prediction network (FNPN) with input candidate variables, selected among easily available measurements (e.g., body mass index (BMI), waist circumstance (WC), and body composition) and OSAS related questionnaires, to rapidly predict an optimal PAP. The FNPN comprises fuzzy rules and is characterized with the ability of automatic rule growing and pruning from training data. A total of 147 participants from April 2018 to April 2019 were enrolled in Taichung Veterans General Hospital, Taiwan. After two selection processes for feature extraction, WC and BMI were the significant variables for entering the FNPN to predict optimal PAP. Experimental results showed that the average successful prediction rate of the proposed method was 71.8%. This study also found that Epworth sleepiness scales (ESS) and body composition, such as visceral fat area and percent body fat, were excluded in the final prediction model. Compared with existing models, the proposed prediction approach provided a rapid prediction of optimal PAP with higher accuracy. [ABSTRACT FROM AUTHOR]