Blind-Stage Search Algorithm for the Key-Variable Search Scheme

Fan Tien Cheng, Chin Yi Lin, Chun Fang Chen, Ren Xiang Xiao, Jing Wen Zheng, Yao Sheng Hsieh

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Yield enhancement is crucial for the companies' profitability performance, especially during the development and mass production phases. So, the root causes of yield loss should be quickly identified in these stages for saving the production cost. To identify the root causes, traditional yield enhancement approaches collect all production-related data for big data analysis. Nevertheless, since the production-related data are often large and complicated, it is difficult to quickly search for the root causes that affect the yield. In light of this, the Key-variable Search Algorithm (KSA) (F.-T. Cheng et al., "A scheme of high-dimensional key-variable search algorithms for yield improvement," IEEE Robot Autom. Lett., vol. 2, no. 1, pp. 179-186, Jan. 2017), was proposed to resolve this problem. The KSA scheme provides users a quick and efficient way to identify the root causes of the yield. However, when a process constituted a single device or when a process has several devices with the same defect density, the KSA cannot make correct identification if the root cause is in the device mentioned above. This is the so-called blind-stage problem. The purpose of this letter is to propose a Blind-stage Search Algorithm for resolving this blind-stage problem.

Original languageEnglish
Article number7934071
Pages (from-to)1840-1847
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
Publication statusPublished - 2017 Oct

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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