Automated classification scheme plus AVM for wafer sawing processes

Yu Ming Hsieh, Rung Lu, Jing Wen Lu, Fan Tien Cheng, Muhammad Adnan

研究成果: Article同行評審

摘要

For the current wafer sawing process, the wafers in the same lot are inspected at the end of the entire process. Therefore, a defect, such as chipping, occurs during processing will only be detected until the end of the process, which is too late and may cause massive defects. If Automatic Virtual Metrology (AVM) is implemented in the wafer sawing process, when chippings occur and are detected, its chipping amount can be predicted by AVM on-line and in real time. Also, AVM's individual similarity index (ISI) analysis can be applied to identify the root cause of chipping. As a result, this root cause can be fixed to avoid generating defects in the subsequent wafers. However, chipping won't happen to all wafers. Since the AVM system deals mainly with the regression problem, it cannot classify whether a wafer is chipped or not. Hence, there is a need to predict wafer-chipping occurrence before applying AVM to the wafer sawing process. To solve the above mentioned problem, the wafer sawing qualitymonitoring is divided into two stages. An Automated Classification Scheme (ACS) based on ensemble learning is developed in Stage I to pre-determine whether a wafer is chipped. If chipping is detected, then proceed to Stage II for the AVM system to predict the chipping amount and identify the root cause that results in this chipping. With the so-calledACS-plus-AVMscheme, theAVMapplication in the wafer sawing process can be realized.

原文English
文章編號9110778
頁(從 - 到)4525-4532
頁數8
期刊IEEE Robotics and Automation Letters
5
發行號3
DOIs
出版狀態Published - 2020 七月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 生物醫學工程
  • 人機介面
  • 機械工業
  • 電腦視覺和模式識別
  • 電腦科學應用
  • 控制和優化
  • 人工智慧

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