Batch process fault monitoring method based on multi-stage ICA-SVDD

An ICA-SVDD, fault monitoring technology, applied in electrical testing/monitoring, testing/monitoring control systems, instruments, etc.

Active Publication Date: 2017-10-20
JIANGNAN UNIV
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Problems solved by technology

Since most data description methods related to principal component analysis and partial least squares analysis have the limitation that the data conform to Gaussian distribution and the relationship between different variables is linear

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  • Batch process fault monitoring method based on multi-stage ICA-SVDD
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  • Batch process fault monitoring method based on multi-stage ICA-SVDD

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Embodiment Construction

[0018] Combine below figure 1 Shown, the present invention is described in further detail:

[0019] The research data used is collected from an actual semiconductor etching process, and the fault monitoring is carried out on the normal data and fault data of semiconductor etching respectively.

[0020] Step 1: Two-dimensional expansion of the three-dimensional data set X (I×J×K) of the batch process, where I represents the number of batches, J represents the number of variables, and K represents the number of sampling points. Using the data processing method that combines the batch direction and the variable direction, the three-dimensional data X (I×J×K) is first transformed into a two-dimensional matrix X(I×KJ) along the batch direction, and then the two-dimensional matrix; and then recombine according to the variable direction to form a new two-dimensional matrix X(KI×J). Two-step data expansion method such as figure 2 shown.

[0021] Step 2: Carry out reasonable stage...

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Abstract

The invention discloses a batch process fault monitoring method based on multi-stage ICA-SVDD, is applied to a batch process having the complex process mechanism and multiple operation stages and aims to solve a multi-stage and data distribution non-Gauss problem existing in the batch processes. An improved stage division and fault monitoring method is employed. The method comprises steps that firstly, stage division is carried out according to similarity of each time segment and a K mean value algorithm, secondly, an independent component analysis method is utilized for each stage to extract the non-gauss characteristic information, and lastly, a support vector data description algorithm is introduced to respectively establish a statistics analysis model for independent components and the residual gauss residual error residual error space, and fault monitoring of the whole process is realized. The method is advantaged in that the method is applied to fault monitoring of the actual semiconductor etching process, and the better monitoring effect for the multi-stage batch processes is realized as proved by the result.

Description

technical field [0001] The invention relates to a multi-stage ICA-SVDD-based intermittent process fault monitoring method, which belongs to the field of industrial process fault diagnosis and soft measurement. Background technique [0002] Batch process is an important industrial production mode, its process mechanism is complicated and there are multiple operation stages, and the product quality is easily affected by uncertain factors. In order to ensure the safe and reliable operation of the batch production process and the pursuit of high-quality products, it is necessary to establish an effective process monitoring system to monitor the faults of the batch production process. Multivariate statistical process control methods have been widely used in batch process monitoring, such as multiway principal component analysis (MPCA) and multiway partial least squares analysis (multiway partial least squares, MPLS). But most of the data description methods related to principal ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0243
Inventor 熊伟丽郑皓陈树
Owner JIANGNAN UNIV
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