Die-to-die Inspection of Semiconductor Wafer using Bayesian Twin Network

Eunjeong Choi, Jeongtae Kim

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this paper, we propose a deep learning-based die-to-die wafer inspection system, which is composed of an encoder-decoder-based twin network (Siamese network). In contrast to other deep learning-based wafer inspection methods, the proposed method takes golden and test die images as input and compares them to detect different areas as defects. In addition, we apply Bayesian learning to improve the performance of the proposed twin network. We verified the performance of the proposed method through experiments using patterned wafer images, which confirmed that the performance could be improved by applying Bayesian learning.

Original languageEnglish
Pages (from-to)382-389
Number of pages8
JournalIEIE Transactions on Smart Processing and Computing
Volume10
Issue number5
DOIs
StatePublished - 2021

Keywords

  • Bayesian learning
  • Die-to-die inspection
  • Machine learning
  • Machine vision
  • Siamese network
  • Twin network
  • Wafer inspection

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