IDENTIFICATION OF DEFECTS IN PRODUCTS USING DEEP LEARNING

Abstract: In contemporary manufacturing, ensuring product quality is paramount. This project introduces Deep Defect Net, a novel deep learning framework designed for the automated identification of defects in manufactured products. The objective is to revolutionize quality control processes by leveraging the capabilities of deep neural networks to discern and classify defects with unprecedented accuracy and efficiency.

Keywords: convolutional neural network (CNN) architectures, Deep learning, Semiconductor,