32‐1: Improving QD Backplane Defect Image Generation Using Automatic Masking in Diffusion Models

Abstract

The modern QD backplane production line deploys deep learning‐based defect classifier, which uses backplane images, to the auto repair system. However, one key challenge in this application is the lack of defect images available for training when a new product is first introduced. To address this issue, generative models like GAN have been used to generate defect images for new products but the image quality and diversity are not optimal due to unstable training and mode collapse. In this paper, the newest diffusion model is applied for defect image generation. It is trained on both normal (defect‐free) and defect images from new Product A and existing Product B. This allows the model to learn defects from Product B and generate synthetic defect images of Product A from normal images. Using an innovative method to automatically apply masks in diffusion sampling, the generated synthetic images are demonstrated to achieve high image quality and good defect diversity while preserving image fidelity outside the defective region when compared to the input normal image. Experiments are also conducted to train classifiers with generated defect images from different sampling methods. The proposed automatic masking method outperforms other sampling methods in the experiments.