A Super-Resolution Model for Improving the Precision of Wafer Mark Alignment

Abstract

Wafer bonding machine uses industrial camera to recognize marks on top wafer and bottom wafer, compute deviation, and move top wafer to align the bottom wafer. The alignment precision mainly depends on the camera resolution, high resolution industrial camera is expensive, while classical image up-sampling methods such as bicubic interpolation don’t have good effect. To improve the alignment precision, a super-resolution model is proposed. Main component of this model is convolutional neural network. The first two convolutional layers are to extract feature on wafer image, the next convolutional layer is used for nonlinear mapping, and the final one outputs super-resolution image. Peak Signal to Noise Ratio (PSNR) is used to evaluate the similarity of super resolution image and the target high resolution image. It’s proved by experiments that the super-resolution model has better effect than classical image interpolation methods. This research result can also be applied to other equipment using industrial cameras.