Scheduling and Control of a Wafer Transfer Robot for Foundry Equipment Innovation Competition

Song, Kai Tai; Ou, Song Qing; Yang, Cheng An; Sun, Yu Xuan; Kang, Li Ren; Wang, Zhen Yu; Wang, Yu Shin; Lu, Pei Chun; Ko, Chun Long; Chen, Yao Hsiang

doi:10.1016/j.ifacol.2019.11.654

Cited by 2 publications

(2 citation statements)

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“…Recently, various studies have been conducted to detect the point of error using data from wafer transfer robots [12][13][14][15]. To find abnormalities in the robot, a study was conducted to analyze the data obtained from the transfer robot [15].…”

Section: Introductionmentioning

confidence: 99%

“…To find abnormalities in the robot, a study was conducted to analyze the data obtained from the transfer robot [15]. Additionally, existing research has scheduled wafer transfer robots by analyzing photos of wafers and using endpoint determination algorithms [14]. However, if the status of the wafer cannot be checked in the middle of the process, scheduling cannot be performed.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Maintenance System for Wafer Transport Robot Using K-Means Algorithm and Neural Network Model

2022

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Maintenance is the technology of continuously monitoring the conditions of equipment and predicting the timing of maintenance for equipment. Particularly in the field of semiconductor manufacturing, where processes are automated, various methods are being tried to minimize the economic losses and maintenance costs caused by equipment failure. A new Predictive Maintenance (PdM) technique, a new method of maintenance, is introduced in this paper to develop an algorithm for predicting the failure of wafer transfer robots in advance. The acceleration sensor data used in the experiment were obtained by installing a sensor onto the wafer transfer robot. To analyze these data, the data preprocessing and FFT process were performed. These data were divided into normal data, first error data, second error data, and third error data (failure data) in stages. By clustering the data using the K-means algorithm, the center point distribution of the clusters was analyzed, and the features of the error data and normal data were extracted. Using these features, an artificial neural network model was designed to predict the point of failure of the robot. Previous research on maintenance systems of the transfer robot used fewer than 50 error data, but 1686 error data were used in this experiment. The reliability of the model is improved by randomly selecting data from a total of 2248 data sets. In addition, it was confirmed that it was possible to classify normal data and error data with an accuracy of 97% and to predict equipment failure by applying neural network modeling.

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