Deep Learning for Semiconductor Defect Classification

Terence Sweeney, Sonya Coleman, Dermot Kerr

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

Abstract

Automated inspection has become a vital part of quality control during semiconductor wafer production. Current processes are focused on finding defects via variation from a ‘golden’ image using pixel to pixel comparisons or utilization of opaque neural network-based approaches. In this paper we present an approach which uses deep learning methods to classify defects on semiconductor die images and show the experimental steps taken in order to produce a highly accurate system based on previous models.
Original languageEnglish
Title of host publication2022 IEEE 20th International Conference on Industrial Informatics (INDIN)
PublisherIEEE
Pages572-577
Number of pages6
ISBN (Electronic)978-1-7281-7568-3
ISBN (Print)978-1-7281-7569-0
DOIs
Publication statusPublished online - 15 Dec 2022
EventIEEE International Conference on Industrial Informatics, - Perth, Australia
Duration: 25 Jul 202228 Jul 2022
https://2022.ieee-indin.org/

Conference

ConferenceIEEE International Conference on Industrial Informatics,
Abbreviated titleINDIN22
Country/TerritoryAustralia
CityPerth
Period25/07/2228/07/22
Internet address

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Defect Detection
  • Defect Classification
  • Semiconductor wafers
  • Image Processing
  • Deep learning
  • Transfer Learning
  • Convolutional Neural Network
  • Convolutional Neural Networks

Fingerprint

Dive into the research topics of 'Deep Learning for Semiconductor Defect Classification'. Together they form a unique fingerprint.

Cite this