Locally Adaptive Statistical Background Modeling with Deep Learning based False Positive Rejection for Defect Detection in Semiconductor Units

Bashar M. Haddad, Samuel F. Dodge, Lina J. Karam, Nital S. Patel, Martin W. Braun

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we present a system for the detection and classification of defects in semiconductor units. The proposed system consists of three stages: proposal generation stage, defect detection stage and refinement stage. In the proposal generation stage, changes on the target unit are detected using a novel change detection approach. In the second stage, a deep neural network is used to classify detected regions into either defective or non-defective regions. Non-defective regions are regions exhibiting allowable changes due to factors such as lighting conditions and subtle differences in manufacturing. The defect detection stage achieves up to 94.3% accuracy. In practice, defects that are smaller than a specified tolerance size are ignored by manufacturers. The tolerance size depends on the defect types and is determined based on risk factors. In order to ignore such defects, our approach includes a final refinement stage wherein the detected defects are categorized by a stacking-based ensemble classifier into different classes. The refined system achieves up to 97.88% overall detection accuracy. The presented system is immediately applicable to different types of defects on die, epoxy and substrate. Inputs of the system can be either color or grayscale images.

Original languageEnglish (US)
JournalIEEE Transactions on Semiconductor Manufacturing
DOIs
StateAccepted/In press - 2020
Externally publishedYes

Keywords

  • Adaptation models
  • Automated inspection systems
  • background modeling
  • background subtraction
  • Computational modeling
  • deep neural network
  • features extraction.
  • Inspection
  • Lighting
  • machine learning
  • Manufacturing
  • Neural networks
  • Substrates

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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