Abstract
Semiconductor manufacturing process data usually have multimodel and multiphase characteristics which do not meet the application assumptions in neighborhood preserving embedding (NPE). Aiming at the above limitations of NPE, a novel monitoring strategy combining the advantages of the neighborhood preserving embedding and Gaussian mixture model(NPE-GMM) is proposed. Firstly, the window data are obtained by the default window width. Next, the score of the current window data set are calculated by NPE. After that, some Gaussian components of the score are determined by GMM. Finally, a quantification index is proposed to monitor process status. NPE-GMM can not only maintain more local structure information of the current window data set in the feature subspace, but also reduce the computational complexity of GMM in fault detection processes. By introducing the new statistic, NPE-GMM can effectively improve the fault detection rate of some multimodel batch processes. The effectiveness of the proposed method is verified in a numerical case and the semiconductor etching process. The simulation results indicated that the proposed method has a higher fault detection rate than traditional methods.
Highlights
With the rapid development of science and technology, it has been recognized that the industrial production process is developing towards automation and integration
A method based on local structure, neighborhood preserving embedding(NPE) is a well-applied linear projection technique [9], [10]
A novel monitoring strategy combining the advantages of the neighborhood preserving embedding and Gaussian mixture model(NPE-GMM) is proposed
Summary
With the rapid development of science and technology, it has been recognized that the industrial production process is developing towards automation and integration. In order to effectively monitor the status of batch processes, some data-driven methods have been proposed and successfully applied [3]–[5]. A method based on local structure, neighborhood preserving embedding(NPE) is a well-applied linear projection technique [9], [10]. C. Zhang et al.: Novel Monitoring Strategy Combining the Advantages of NPE and GMM a semibatch reactor for the production of styrene-butadiene latex [16], multiway kernel PCA(MKPCA) proposed by Lee et al is applied in a simulation benchmark of fed-batch penicillin production [17]. A novel monitoring strategy combining the advantages of the neighborhood preserving embedding and Gaussian mixture model(NPE-GMM) is proposed.
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