TY - GEN
T1 - The Prediction of Positioning shift for a Robot Arm Using Machine Learning Techniques
AU - Huang, Ping Wun
AU - Chung, Kuan Jung
PY - 2019/10
Y1 - 2019/10
N2 - This study presents an Artificial Intelligence (AI) approach for estimating the Cartesian positioning shift of a wafer handling robot arm to prevent the occurrence of unexpected event, drop of wafers. First, a Charge-coupled Device (CCD) based robot arm fault diagnostic system was built to measure the target positions of the robot arm when handling wafers. An ensemble-based machine learning model with time series cross validation technique from a commercial software called Decanter AI (Mobagel Inc.) was applied to predict the quantity of the maximum position shift with respect to X and Y axis for next one minute. The prediction results by the test datasets through 38,417 minutes show that the Root Mean Square Error (RMSE) is 4.351 μm to validate the trained model is appropriate for predicting the positioning shift of the handling robot arm.
AB - This study presents an Artificial Intelligence (AI) approach for estimating the Cartesian positioning shift of a wafer handling robot arm to prevent the occurrence of unexpected event, drop of wafers. First, a Charge-coupled Device (CCD) based robot arm fault diagnostic system was built to measure the target positions of the robot arm when handling wafers. An ensemble-based machine learning model with time series cross validation technique from a commercial software called Decanter AI (Mobagel Inc.) was applied to predict the quantity of the maximum position shift with respect to X and Y axis for next one minute. The prediction results by the test datasets through 38,417 minutes show that the Root Mean Square Error (RMSE) is 4.351 μm to validate the trained model is appropriate for predicting the positioning shift of the handling robot arm.
UR - http://www.scopus.com/inward/record.url?scp=85082695988&partnerID=8YFLogxK
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U2 - 10.1109/IMPACT47228.2019.9024996
DO - 10.1109/IMPACT47228.2019.9024996
M3 - Conference contribution
AN - SCOPUS:85082695988
T3 - Proceedings of Technical Papers - International Microsystems, Packaging, Assembly, and Circuits Technology Conference, IMPACT
SP - 58
EP - 61
BT - IMPACT 2019 - 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference, Proceeding
PB - IEEE Computer Society
T2 - 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference, IMPACT 2019
Y2 - 23 October 2019 through 25 October 2019
ER -