This technology aims to automatically extractdefine features from a data-driven perspective through a series of data engineering, machine learningensemble learning technologies via the big data collected from equipment sensorsquality characteristic measurement results. For products that have no quality measurement, a virtual metrology resultpredicted confidence score are provided as a decision-making basis for process control.

In the past, when using device sensing data for virtual metrology, it was necessary to incorporate domain knowledge to define key intervalsstatistics of the profile in advance for analysis. This technology integrates statistics, machine learning,decision analysis methods, automatically defines key intervalsconverts them into interpretable features, combines unsupervised clustering methods to reduce errors, constructs predictive models,calculates confidence scores for predicted values.

This technology takes high-tech manufacturing as the priority to be introduced. According to the development status of other manufacturing industries, after completing the external sensor of the equipmentimproving the information system to link the production historyquality characteristics, it can be imported for application. Because this technology does not need to rely too much on domain knowledge,traditional industries can use this to gradually carry out digital transformationmaintain corporate competitiveness.

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