Method for prognostic maintenance in semiconductor manufacturing equipments
First Claim
1. A method for prognostic maintenance in semiconductor manufacturing equipments, comprising the steps of:
- collecting a plurality of raw data and preprocessing the plurality of collected raw data to filter out meaningless null detection values existing in the plurality of raw data and generate detection values of normal pattern;
performing classification through a statistic classification model on the plurality of preprocessed raw data to generate a plurality of health indices;
performing classification on the plurality of generated health indices by a prescribed classification method to generate a plurality of health information;
using a regression analysis method to process the plurality of health information to generate a plurality of health reports; and
performing repairs and maintenance actively by in-situ engineers based on the plurality of generated health reports.
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Abstract
A method for prognostic maintenance in semiconductor manufacturing equipments is disclosed. The said method comprising: collecting a plurality of raw data from the default detection and classification system for equipments, preprocessing the raw data, using the neural network model (NN model) to find a plurality of health indices, generating health information by using the principal component analysis (PCA) to identify the health indices, and using the partial least square discriminated analysis (PLS-DA) to find a health report. The health report provides the engineers with current risk levels of equipments. By the health report, the engineers can initiate prognostic maintenance and repair the equipments early.
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Citations
18 Claims
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1. A method for prognostic maintenance in semiconductor manufacturing equipments, comprising the steps of:
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collecting a plurality of raw data and preprocessing the plurality of collected raw data to filter out meaningless null detection values existing in the plurality of raw data and generate detection values of normal pattern; performing classification through a statistic classification model on the plurality of preprocessed raw data to generate a plurality of health indices; performing classification on the plurality of generated health indices by a prescribed classification method to generate a plurality of health information; using a regression analysis method to process the plurality of health information to generate a plurality of health reports; and performing repairs and maintenance actively by in-situ engineers based on the plurality of generated health reports. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for prognostic maintenance in semiconductor manufacturing equipments, comprising the steps of:
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collecting a plurality of raw data, the plurality of raw data being provided by a Fault Detection and Classification (FDC) system, and the plurality of raw data indicate variation values detected in real-time on each wafer by the FDC system during semiconductor processes, and preprocessing the plurality of collected raw data to filter out meaningless null detection values existing in the plurality of raw data and to generate detection values of normal pattern; performing classification through a Neural Network (NN) model on the plurality of preprocessed raw data to generate a plurality of health indices; performing classification on the plurality of generated health indices by the Principal Component Analysis (PCA) to generate a plurality of health information; using the Partial Least Squares Discriminated Analysis (PLS-DA) to process the plurality of health information to generate a plurality of health reports; and performing repairs and maintenance actively by in-situ engineers based on the plurality of generated health reports.
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12. A method for prognostic maintenance in semiconductor manufacturing equipments, comprising the steps of:
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collecting a plurality of raw data and preprocessing the plurality of collected raw data, the plurality of raw data consisting of a plurality of historic data and a plurality of newly added data, in which the plurality of historic data indicate the data outputted by the semiconductor equipments under healthy condition, the plurality of newly added data represent the data outputted by the semiconductor equipment under unknown condition; performing classification through a Neural Network Model (NN Model), on the plurality of preprocessed raw data to generate a plurality of health indices; performing classification on the plurality of generated health indices by a prescribed classification method to generate a plurality of health information; using a regression analysis method to process the plurality of health information to generate a plurality of health reports; and
performing repairs and maintenance actively by in-situ engineers based on the plurality of generated health reports. - View Dependent Claims (13, 14, 15, 16)
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17. A method for prognostic maintenance in semiconductor manufacturing equipments, comprising the steps of:
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collecting a plurality of raw data and preprocessing the plurality of collected raw data; performing classification through a statistic classification model on the plurality of preprocessed raw data to generate a plurality of health indices; performing classification on the plurality of generated health indices by Principal Component Analysis (PCA) to generate a plurality of health information, wherein the plurality of health indices consists of; performing a linear conversion operation on the plurality of health indices for classification, in which the linear conversion operation converts the plurality of health indices in an original coordinate system into a new coordinate system, and the new coordinate system has a plurality of new coordinate axes, and the plurality of new coordinate axes are respectively the first new axis, the second new axis, . . . , and the Nth new axis; finding projection values of the plurality of health indices projected onto the plurality of new axes, acquiring a plurality of first principal component values over the first new axis, a plurality of second principal component values over the second new axis, . . . , and a plurality of Nth principal component values over the Nth new axis; processing the plurality of first principal component values, plurality of second principal component values, . . . , and plurality of Nth principal component values based on a plurality of confidence indices built by in-situ engineers to acquire a plurality of principal component characteristic values; and generating a plurality of health information according to the plurality of principal component characteristic values; using a regression analysis method to process the plurality of health information to generate a plurality of health reports; and performing repairs and maintenance actively by in-situ engineers based on the plurality of generated health reports. - View Dependent Claims (18)
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Specification