Laser-based Hair Crack Detection on Wafers

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

The detection of hair cracks is one of the key challenges to improve wafer-processing stability. Contrary to other defects on the wafer-edge, hair cracks have a very small geometric footprint, making them hard to detect for measurement systems. This raises the demand for a powerful data analysis tool, which can extract the relevant information even in low signal-To-noise ratio scenarios. In this paper, we investigate an approach for hair crack detection using a laser-based wafer edge inspection device and deep neural networks to analyze and classify the measured data. We propose different pre-processing methods for the raw measurement data, to improve the learning behavior of the networks. The results show that a substantial improvement, in both detection rate and false positive rate, can be achieved by appropriate pre-processing of the measured data.

Original languageEnglish
Title of host publication2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2020
Number of pages6
ISBN (Electronic)9781728158761
DOIs
Publication statusPublished - Aug 2020
Event2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference - Virtuell, United States
Duration: 24 Aug 201926 Aug 2019

Publication series

NameASMC (Advanced Semiconductor Manufacturing Conference) Proceedings
Volume2020-August
ISSN (Print)1078-8743

Conference

Conference2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference
Abbreviated titleASMC 2020
CountryUnited States
CityVirtuell
Period24/08/1926/08/19

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Engineering(all)
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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