Engineering

Transfer of learning

Systems engineering

Contextual image classification

Data mining

Parallel computing

Computer science

Semiconductor

Manufacturing engineering

Transfer (computing)

Semiconductor industry

Physics

Task (project management)

Pattern recognition (psychology)

Mathematics

Optics

Image (mathematics)

Wafer

Electronic engineering

Focus (optics)

Artificial intelligence

Geometry

Deep learning

Artificial neural network

Semiconductor device fabrication

Convolutional neural network

Machine learning

Reduction (mathematics)

Electrical engineering

IEEE Transactions on Semiconductor Manufacturing

In this paper, we focus on a defect analysis task that requires engineers to identify the causes of yield reduction from defect classification results.We organize the analysis work into three phases: defect classification, defect trend monitoring and detailed classification.To support the first and third engineer's analytical work, we use a convolutional neural network based on the transfer learning method for automatic defect classification.We evaluated our proposed methods on real semiconductor fabrication data sets by performing a defect classification task using a scanning electron microscope image and thoroughly examining its performance.We concluded that the proposed method can classify defect images with high accuracy while lowering labor costs equivalent to one-third the labor required for manual inspection work.

A CNN-Based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing