摘要
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.
| 原文 | English |
|---|---|
| 文章編號 | 7498656 |
| 頁（從 - 到） | 179-186 |
| 頁數 | 8 |
| 期刊 | IEEE Robotics and Automation Letters |
| 卷 | 2 |
| 發行號 | 1 |
| DOIs | |
| 出版狀態 | Published - 2017 一月 1 |
指紋
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
引用此文
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A scheme of high-dimensional key-variable search algorithms for yield improvement. / Cheng, Fan Tien; Hsieh, Yao Sheng; Zheng, Jing Wen; Chen, Szu Min; Xiao, Ren Xiang; Lin, Chin Yi.
於: IEEE Robotics and Automation Letters, 卷 2, 編號 1, 7498656, 01.01.2017, p. 179-186.研究成果: Article
TY - JOUR
T1 - A scheme of high-dimensional key-variable search algorithms for yield improvement
AU - Cheng, Fan Tien
AU - Hsieh, Yao Sheng
AU - Zheng, Jing Wen
AU - Chen, Szu Min
AU - Xiao, Ren Xiang
AU - Lin, Chin Yi
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
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U2 - 10.1109/LRA.2016.2584143
DO - 10.1109/LRA.2016.2584143
M3 - Article
AN - SCOPUS:85044974142
VL - 2
SP - 179
EP - 186
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 1
M1 - 7498656
ER -