A scheme of high-dimensional key-variable search algorithms for yield improvement

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

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


Product yield directly affects production cost. Thus manufacturers seek to quickly enhance product yield during the development and mass-production processes. In other words, when a yield loss occurs, the root causes should be found rapidly in both the development and mass-production phases. When a yield loss is encountered, the traditional yield enhancement approach is to collect all production-related data to perform big data analysis in order to find out the root causes of affecting yield and remedy them. However, production-related data are extremely large and complicated, which makes it hard to search for the root causes of a yield loss. This leads to the problem of high-dimensional variable selection. To solve this problem, a scheme of Key-variable Search Algorithms (KSAs) is proposed in this paper. The inputs of this KSA scheme include production routes, process data, inline data, defects, and final inspection results; while the outputs are search results and their corresponding reliance indices. The search results are the key stages that cause the yield loss or the key variables that are the root causes of the yield loss. The thin film transistor-liquid crystal display (TFT-LCD) process is adopted as the illustrative example to demonstrate the KSA scheme.

Original languageEnglish
Article number7498656
Pages (from-to)179-186
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number1
Publication statusPublished - 2017 Jan

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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