A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks

TL;DR

This paper presents a novel multi-stage deep neural network system, called SH-CNN, for highly detailed defect detection and classification in semiconductor manufacturing, improving accuracy and efficiency over existing methods.

Contribution

It introduces a stacked hybrid CNN architecture inspired by visual attention mechanisms, capable of detecting minute defect structures in high-resolution images.

Findings

SH-CNN outperforms current learning-based inspection methods

The multi-stage approach enables early defect pattern elimination

Focus on detail levels improves classification accuracy

Abstract

Automated visual inspection in the semiconductor industry aims to detect and classify manufacturing defects utilizing modern image processing techniques. While an earliest possible detection of defect patterns allows quality control and automation of manufacturing chains, manufacturers benefit from an increased yield and reduced manufacturing costs. Since classical image processing systems are limited in their ability to detect novel defect patterns, and machine learning approaches often involve a tremendous amount of computational effort, this contribution introduces a novel deep neural network based hybrid approach. Unlike classical deep neural networks, a multi-stage system allows the detection and classification of the finest structures in pixel size within high-resolution imagery. Consisting of stacked hybrid convolutional neural networks (SH-CNN) and inspired by current approaches of visual attention, the realized system draws the focus over the level of detail from its structures to more task-relevant areas of interest. The results of our test environment show that the SH-CNN outperforms current approaches of learning-based automated visual inspection, whereas a distinction depending on the level of detail enables the elimination of defect patterns in earlier stages of the manufacturing process.