Commonality Analysis for Detecting Failures Caused by Inspection Tools in Semiconductor Manufacturing Processes

Dae Woong An, Seung Kim, Hyun Kyu Kim, Chang Ouk Kim

Research output: Contribution to journalArticlepeer-review


Semiconductor fabrication involves hundreds of process steps through various manufacturing tools. These processing steps are composed of many manufacturing and inspection steps. Inspection is an important step in the fabrication process to determine whether a process is in or out of control. Abrupt manufacturing or inspection tool excursion can lead to a serious low yield problem. Although commonality analysis is a proven tool for detecting abrupt tool excursion, it has gained only limited success in detecting manufacturing tool excursion outside of inspection tools. Compared with manufacturing tools, only a small number of lots or wafers pass through inspection tools. Therefore, it is difficult to construct a sufficient lot history log for inspection commonality analysis in contrast to that of manufacturing tools. Furthermore, inspection may stress a wafer during its own processing, therefore, the target wafer is changed sequentially or randomly. Accordingly, a lot history is apt to include missing traces, which hinders finding inspection tool excursion effectively. In this paper, we propose a comparative analysis framework for commonality analysis algorithms. Performance measures are suggested. To compare the performance of the algorithms effectively, we use a synthetically generated dataset in a simulation experiment. In addition, we apply the algorithms to a real problem that occurred in the fabrication process. Our proposed algorithm demonstrates superiority over the other commonality analysis algorithms in the experiments.

Original languageEnglish
Pages (from-to)596-604
Number of pages9
JournalIEEE Transactions on Semiconductor Manufacturing
Issue number4
Publication statusPublished - 2022 Nov 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIT) under Grant NRF-2022R1A2B5B01001889.

Publisher Copyright:
© 2022 IEEE.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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