Semiconductor Defect Pattern Classification by Self-Proliferation-and-Attention Neural Network

TL;DR

This paper introduces SP&A-Net, a novel neural network architecture combining self-proliferation and attention mechanisms, designed for efficient and accurate semiconductor defect classification suitable for edge devices in IoT-enabled manufacturing.

Contribution

The paper proposes a new neural network architecture that enhances defect classification efficiency and accuracy by integrating self-proliferation and attention mechanisms, optimized for edge deployment.

Findings

Higher accuracy than existing methods

Lower computational cost

Effective in various defect classification tasks

Abstract

Semiconductor manufacturing is on the cusp of a revolution: the Internet of Things (IoT). With IoT we can connect all the equipment and feed information back to the factory so that quality issues can be detected. In this situation, more and more edge devices are used in wafer inspection equipment. This edge device must have the ability to quickly detect defects. Therefore, how to develop a high-efficiency architecture for automatic defect classification to be suitable for edge devices is the primary task. In this paper, we present a novel architecture that can perform defect classification in a more efficient way. The first function is self-proliferation, using a series of linear transformations to generate more feature maps at a cheaper cost. The second function is self-attention, capturing the long-range dependencies of feature map by the channel-wise and spatial-wise attention mechanism. We named this method as self-proliferation-and-attention neural network. This method has been successfully applied to various defect pattern classification tasks. Compared with other latest methods, SP&A-Net has higher accuracy and lower computation cost in many defect inspection tasks.