Occurrence Prediction of Dislocation Regions in Photoluminescence Image of Multicrystalline Silicon Wafers Using Transfer Learning of Convolutional Neural Network

Abstract

In this paper, we evaluate a prediction method of regions including dislocation clusters which are crystallographic defects in a photoluminescence (PL) image of multicrystalline silicon wafers. We applied a method of a transfer learning of the convolutional neural network to solve this task. For an input of a sub-region image of a whole PL image, the network outputs the dislocation cluster regions are included in the upper wafer image or not. A network learned using image in lower wafers of the bottom of dislocation clusters as positive examples. We experimented under three conditions as negative examples; image of some depth wafer, randomly selected images, and both images. We examined performances of accuracies and Youden's J statistics under 2 cases; predictions of occurrences of dislocation clusters at 10 upper wafer or 20 upper wafer. Results present that values of accuracies and values of Youden's J are not so high, but they are higher results than ones of bag of features (visual words) method. For our purpose to find occurrences dislocation clusters in upper wafers from the input wafer, we obtained results that randomly select condition as negative examples is appropriate for 10 upper wafers prediction, since its results are better than other negative examples conditions, consistently.