IEEE 6th International Conference Engineering & Telecommunication – En&T-2019

Improvement of the quality of pattern recognition method in conditions of a deficient amount of labeled experimental data

• Method of preparing the composite training dataset:

  • review of typical manufacturing causes of defect patterns;
  • procedure of synthesized wafe maps creation;
  • adaptive configuration of training dataset.

• New learning DCNN strategy:

  • pretrain stage on pure synthetic dataset;
  • main train stage on composite dataset.

• Numerical calculations and results:

  • DCNN model training: VGG-19, ResNet-50, ResNet-34 and MobileNetV2;
  • experimental comparison of models accuracy on different conditions;
  • dependence of classification accuracy on amount of experimental data.

Source of experimental data

• Proposal of the method of preparing the composite training dataset

• Development of the new learning DCNN model strategy which improve the final result of accuracy by 1% up to 4%

• Experimental accuracy comparison of VGG-19, ResNet-50, ResNet-34 and MobileNetV2 DCNN models for the different ratio of experimental labeled data to synthesized data

• Achievement of 87.8% final classification accuracy with Rₗₛ = 0.05 on the public dataset WM-811K by ResNet-50

• Formative evaluation of needed amount of experimental data to obtain required accuracy