National Cheng-Kung University

Yu-Ming Hsieh

Fan-Tien Cheng

Chin-Yi Lin

Hsien-Cheng Huang

Ching-Kang Ing

Yueh-Feng Tsai

Li-Hsuan Peng

Jing-Wen Lu

Po-Hsiang Peng

Tan-Ju Wang

A virtual metrology method based on convolutional autoencoder and transfer learning includes performing a data alignment operation, a modeling operation and a calculating operation. The data alignment operation includes performing a data-length adjusting operation onto a plurality of sets of process data. The modeling operation includes classifying paired data and unpaired process data; creating a pre-trained model by using the unpaired process data, and then inputting the paired data to the pre-trained model to create a virtual metrology model based on convolutional autoencoder. The virtual metrology model based on convolutional autoencoder includes at least one convolutional neural network model. In addition, the calculating operation includes executing one of a predicting step and a transfer learning step according to whether the actual metrology data is obtained, thereby calculating one of a phase-one virtual metrology value and a phase-two virtual metrology value.

Virtual metrology method based on convolutional autoencoder and transfer learning and system thereof

A golden path search method for manufacturing process provides a two-phase process to search for a golden path. A first phase step of the two-phase process includes preparing a search model based on a search algorithm, and selecting a plurality of key process stages of a plurality of process stages by feeding sets of final inspection values and the production paths of the workpieces into the searching model, and then generating a plurality of key paths according to the key process stages. A second phase step of the two-phase process includes building a plurality of prediction models of the key paths according to the production paths and the sets of final inspection values, and predicting a plurality of yield rates corresponding to the key paths according to the prediction models, and then searching for the golden path of the key paths according to the yield rates.

Golden path search method for manufacturing process and system thereof

Embodiments of the present invention provide a multiple-variable predictive maintenance method for a component of a production tool and a computer program product thereof, in which a multiple-variable time series prediction (TSP) and an information criterion algorithm are adapted to build a best vector autoregression model (VAR), thereby forecasting the complicated future trend of accidental shutdown of the component of the production tool. Therefore, the multiple-variable prediction of the present invention can improve the accuracy of prediction compared with the single-variable prediction.

Multiple-variable predictive maintenance method for component of production tool and non-transitory tangible computer readable recording medium thereof

A virtual metrology method using a convolutional neural network (CNN) is provided. In this method, a dynamic time warping (DTW) algorithm is used to delete unsimilar sets of process data, and adjust the sets of process data to be of the same length, thereby enabling the CNN to be used for virtual metrology. A virtual metrology model of the embodiments of the present invention includes several CNN models and a conjecture model, in which plural inputs of the CNN model are sets of time sequence data of respective parameters, and plural outputs of the CNN models are inputs to the conjecture model.

Virtual metrology method using convolutional neural network and computer program product thereof

Embodiments of the present disclosure provide a method for predicting an occurrence of a tool processing event, thereby determining whether to activate a virtual metrology. In a model-building stage, plural sets of model-building data are used to create at least one classification model in accordance with at least one classification algorithm, in which each classification model includes plural decision trees. Then, probabilities of the decision trees are used to create at least one reliance index model, and the sets of model-building data are used to create at least one similarity index model in accordance with a statistical distance algorithm. In a conjecture stage, a set of processing data of a workpiece is inputted into each classification model, each reliance index model and each similarity index model to determine whether to activate (start) virtual metrology.

Method for predicting occurrence of tool processing event and virtual metrology application and computer program product thereof

Embodiments of the present invention provide a predictive maintenance method for a component of a production tool, in which a time series prediction (TSP) algorithm and an information criterion algorithm are adapted to build a TSP model, thereby forecasting the complicated future trend of accidental shutdown of the component of the production tool. In addition, an alarm scheme is provided for performing maintenance immediately when the component is very likely to enter a dead state, and a death related indicator (DCI) is provided for quantitatively showing the possibility of the component entering the dead state.

Predictive maintenance method for component of production tool and computer program product thererof

Embodiments of the present disclosure provide a two-phase process for searching root causes of a yield loss in a production line. In a first phase, an interaction between two process tools, that between two parameters, or that between one process tool and one parameter that is likely to cause the yield loss is identified. In a second phase, a threshold of the parameter that is likely to cause the yield loss and is obtained from the first phase is identified. In each phase, two different algorithms can be used to generate a reliance index (RI) for gauging the reliance levels of their search results.

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Kaohsiung, Taiwan

Yu-Ming Hsieh