# DAS-YOLOv13: Dual-Axis Attention and Feature Fusion Model for Wafer Surface Defect Detection

## TL;DR

This paper introduces DAS-YOLOv13, a new model for detecting tiny defects on wafers in semiconductor manufacturing, improving detection accuracy and reliability.

## Contribution

The novel DAS-YOLOv13 model integrates dual-axis attention, adaptive multi-scale representation, and self-modulation feature aggregation for enhanced wafer defect detection.

## Key findings

- DAS-YOLOv13 achieves a mean Average Precision (mAP) of 74.2% on the wafer defect dataset.
- The model improves detection accuracy for tiny and multi-scale defects by 4.3% compared to YOLOv13n.
- It reaches an Average Precision at an Intersection over Union (IoU) threshold of 50% (mAP50) of 92.9%.

## Abstract

What are the main findings?
This paper proposes a dual-axis attention-enhanced YOLOv13 framework to suppress lithographic textures and enhance direction-sensitive tiny wafer defect features.This paper introduces adaptive dynamic multi-scale representation and self-modulation feature aggregation to improve cross-scale feature alignment and fine-grained defect representation.

This paper proposes a dual-axis attention-enhanced YOLOv13 framework to suppress lithographic textures and enhance direction-sensitive tiny wafer defect features.

This paper introduces adaptive dynamic multi-scale representation and self-modulation feature aggregation to improve cross-scale feature alignment and fine-grained defect representation.

What are the implications of the main findings?
More accurate and stable detection of tiny and multi-scale wafer surface defects can be achieved, even under complex industrial backgrounds.This paper provides a lightweight and deployable defect detection solution suitable for high-precision semiconductor automated inspection systems.

More accurate and stable detection of tiny and multi-scale wafer surface defects can be achieved, even under complex industrial backgrounds.

This paper provides a lightweight and deployable defect detection solution suitable for high-precision semiconductor automated inspection systems.

Wafer defects in semiconductor manufacturing can directly damage the physical structure and circuit integrity of wafers, leading to the functional failure of chips. To address this problem, this paper proposes a Dual-Axis Attention-enhanced multi-scale fusion You Only Look Once version 13 (DAS-YOLOv13) model. Based on YOLOv13n, the model is specifically designed for the fast and accurate detection of tiny, multi-scale defects on wafer surfaces. It integrates innovative components such as a dual-axis attention module, an adaptive dynamic multi-scale representation module, and a self-modulation feature aggregation module. By enhancing salient feature expression, improving cross-scale representation capability, and optimizing deep semantic fusion strategies, the model achieves effective defect detection. On the wafer defect dataset, the DAS-YOLOv13 model achieves a mean Average Precision (mAP) of 74.2%, which is 4.3% higher than that of YOLOv13n; the Average Precision at an Intersection over Union (IoU) threshold of 50% (mAP50) reaches 92.9%. The results demonstrate that DAS-YOLOv13 effectively improves the detection accuracy of tiny, multi-scale defects through structural optimization. It provides a reliable solution for high-precision wafer detection in semiconductor manufacturing and can be seamlessly integrated into high-precision semiconductor automated inspection scenarios.