TY - GEN
T1 - What are the most informative data for virtual metrology? a use case on multi-stage processes fault prediction
AU - Maggipinto, Marco
AU - Susto, Gian Antonio
AU - Zocco, Federico
AU - McLoone, Sean
PY - 2019/9/19
Y1 - 2019/9/19
N2 - In recent years, Data intensive technologies have become widespread in semiconductor manufacturing. In particular, Virtual Metrology (VM) solutions had proliferated for quality, control and sampling optimization purposes. VM solutions provide estimations of costly measures from already available data, allowing cost reduction and increased throughput. While most of the literature in VM is focused on providing the most accurate methodological approach in terms of prediction accuracy, no work has previously investigated which are the most informative data for VM. This is particularly relevant since literature is divided between VM based on Optical Emission Spectroscopy (OES) and Key Parameter Indicators (KPI) data. In this work we provide a comparison of between VM based on OES and KPIs on a real case study related to a multi-stage modeling problem.
AB - In recent years, Data intensive technologies have become widespread in semiconductor manufacturing. In particular, Virtual Metrology (VM) solutions had proliferated for quality, control and sampling optimization purposes. VM solutions provide estimations of costly measures from already available data, allowing cost reduction and increased throughput. While most of the literature in VM is focused on providing the most accurate methodological approach in terms of prediction accuracy, no work has previously investigated which are the most informative data for VM. This is particularly relevant since literature is divided between VM based on Optical Emission Spectroscopy (OES) and Key Parameter Indicators (KPI) data. In this work we provide a comparison of between VM based on OES and KPIs on a real case study related to a multi-stage modeling problem.
U2 - 10.1109/COASE.2019.8842942
DO - 10.1109/COASE.2019.8842942
M3 - Conference contribution
AN - SCOPUS:85072975757
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1796
EP - 1801
BT - 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PB - IEEE Computer Society
T2 - 15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Y2 - 22 August 2019 through 26 August 2019
ER -