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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]

Product delivery time is as vital as the yield rate for semiconductor manufacturing companies since the industry has become highly competitive and more dynamic nowadays. In the manufacturing process, appropriate parameter settings can shorten the machine cycle time and in turn the product delivery time. This paper presents the application of 'IF-THEN' rules extracted from a multilayer perceptron (MLP) artificial neural network to tune moulding machine parameters to improve the machine cycle time. We can extract the 'IF-THEN' rules by scrutinising the connecting weights inside the MLP network. Historical genuine operating data of a moulding machine input parameters and cycle time were collected from an integrated circuit packaging company in Taiwan. The data were fitted in with an MLP network, and the 'IF-THEN' rules were extracted afterwards. The 'IF-THEN' rules not only indicate which input parameters dominate the machine cycle time but also explain how to tune them. We have applied the extracted rules to tune the moulding machine parameters. Practical results show that the moulding machine cycle time agrees very well with the extracted rules, and also justify the feasibility of the proposed method. [ABSTRACT FROM PUBLISHER]