Virtual metrology for run-to-run control in semiconductor manufacturing

Pilsung Kang, Dongil Kim, Hyoung Joo Lee, Seungyong Doh, Sungzoon Cho

Research output: Contribution to journalArticlepeer-review

97 Scopus citations


In semiconductor manufacturing processes, run-to-run (R2R) control is used to improve productivity by adjusting process inputs run by run. A process will be controlled based on information obtained during or after a process, including metrology values of wafers. Those metrology values, however, are usually available for only a small fraction of sampled wafers. In order to overcome the limitation, one can use virtual metrology (VM) to predict metrology values of all wafers, based on sensor data from production equipments and actual metrology values of sampled wafers. In this paper, we develop VM prediction models using various data mining techniques. We also develop a VM embedded R2R control system using the exponentially weighted moving average (EWMA) scheme. The experiments consist of two parts: (1) verifying VM prediction models with actual production equipments data, and (2) conducting simulations of the R2R control system. Our VM prediction models are found to be accurate enough to be directly implemented in actual manufacturing processes. The simulation results show that the VM embedded R2R control system improves productivity.

Original languageEnglish
Pages (from-to)2508-2522
Number of pages15
JournalExpert Systems with Applications
Issue number3
StatePublished - Mar 2011


  • Data mining
  • Exponentially weighted moving average controller
  • Photolithography
  • Process control
  • Run-to-run control
  • Semiconductor manufacturing
  • Virtual metrology


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