Knowledge Distillation Cross Domain Diffusion Model: A Generative AI Approach for Defect Pattern Segmentation

Abstract

In semiconductor manufacturing, defect detection is pivotal for enhancing productivity and yield. This paper introduces a novel weakly supervised method, the Implicit Cross Domain Diffusion Model (ICDDM), designed to tackle defect pattern segmentation challenges in the absence of detailed pixel-wise annotations. ICDDM employs a generative model to estimate the joint distribution of images depicting defect patterns and background circuits, formulating this estimation as a Markov Chain and optimizing it through denoising score matching. Building on this, we propose the Cross Domain Latent Diffusion Model (CDLDM), inspired by the Latent Diffusion Model, which simplifies the diffusion process into a lower-dimensional latent space to boost detection efficiency. Further enhancing our model, we introduce the Knowledge Distillation Cross Domain Diffusion Model (KDCDDM), which utilizes CDLDM as a teacher model and a Generative Adversarial Network (GAN) as a student model. This approach significantly accelerates the diffusion process by reducing the number of necessary denoising iterations while maintaining robust model performance. This suite of techniques offers a comprehensive solution for efficient and effective defect detection in semiconductor production environments.