Chip Pad Inspection Method Based on an Improved YOLOv5 Algorithm

Abstract

Chip pad inspection is of great practical importance for chip alignment inspection and correction. It is one of the key technologies for automated chip inspection in semiconductor manufacturing. When applying deep learning methods for chip pad inspection, the main problem to be solved is how to ensure the accuracy of small target pad detection and, at the same time, achieve a lightweight inspection model. The attention mechanism is widely used to improve the accuracy of small target detection by finding the attention region of the network. However, conventional attention mechanisms capture feature information locally, which makes it difficult to effectively improve the detection efficiency of small targets from complex backgrounds in target detection tasks. In this paper, an OCAM (Object Convolution Attention Module) attention module is proposed to build long-range dependencies between channel features and position features by constructing feature contextual relationships to enhance the correlation between features. By adding the OCAM attention module to the feature extraction layer of the YOLOv5 network, the detection performance of chip pads is effectively improved. In addition, a design guideline for the attention layer is proposed in the paper. The attention layer is adjusted by network scaling to avoid network characterization bottlenecks, balance network parameters, and network detection performance, and reduce the hardware device requirements for the improved YOLOv5 network in practical scenarios. Extensive experiments on chip pad datasets, VOC datasets, and COCO datasets show that the approach in this paper is more general and superior to several state-of-the-art methods.

Smoke Detection of Marine Engine Room Based on a Machine Vision Model (CWC-Yolov5s)

“…weight, and meets the requirements of smoke detection [28]. Therefore, it is feasible to select YOLOv5 target detection model for smoke detection in this paper.…”

“…Therefore, most scholars use the YOLO model for smoke detection research. The YOLOv5 target detection model, as the current mainstream single-stage target detection model, has the characteristics of high performance and light weight, and meets the requirements of smoke detection [28]. Therefore, it is feasible to select YOLOv5 target detection model for smoke detection in this paper.…”

Study of a Machine Vision Approach to Leak Monitoring of a Marine System

“…Classical two-stage detection algorithms include R-CNN [12] and Faster R-CNN [13]. Although that method has high detection accuracy, it cannot meet the requirements of real-time detection due to high training costs, slow detection speed, and network depth [14]. The main one-stage object detection algorithm is the YOLO [15][16][17][18] family, which has the advantage of fast detection speed because the depth of the network and the number of parameters are smaller than in the two-stage network.…”