An Auto Chip Package Surface Defect Detection Based on Deep Learning

发布时间：2026-03-01 21:02

利用深度强化学习（Deep Reinforcement Learning）解决复杂决策问题 #生活技巧# #学习技巧# #深度学习技巧#

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Abstract:

Defect detection in chip packaging is a crucial step to ensure product quality and reliability. Traditional methods typically employ image-processing techniques for defec...Show More

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Abstract:

Defect detection in chip packaging is a crucial step to ensure product quality and reliability. Traditional methods typically employ image-processing techniques for defect detection during the chip manufacturing process. However, these solutions require manual feature extraction and have limited adaptability to complex scenarios. Thus, deep-learning (DL)-based methods have received widespread attention. Nevertheless, they may fail to achieve the requirements of real-time and high accuracy, and effective datasets are still missing. In this article, we construct a new chip package surface defect detection dataset, which contains 2919 images and four common defect types. To our knowledge, it is the only dataset for simultaneous detection of multiple chips. Also, we propose a real-time chip package surface defect detection method based on the you only look once version 7 (YOLOv7) model to solve the challenge of detecting small targets. In particular, we utilize k -means++ to recluster the anchor frames, merge the convolutional block attention module (CBAM) attention mechanism and receptive field block (RFB) structure, as well as replace traditional nonmaximum suppression (NMS) with our newly proposed confidence propagation cluster (CP-Cluster) to further increase detection accuracy and result confidence. Finally, we evaluate our method by performing many ablation experiments on the dataset we created. The experimental results demonstrate that compared to the original YOLOv7, the proposed method improves the mean average [email protected] ([email protected]) by 1.39%, the speed of detection by 21.6%, reduces the amount of computation by 17.7%, and the number of parameters by 66.4%, respectively. This proves the superiority and practicality of the proposed method.

Article Sequence Number: 3507115

Date of Publication: 28 December 2023

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I. Introduction

With the continuous development of modern society and economy, the demand for semiconductor chips is rapidly increasing. The total revenue of the global semiconductor market exceeded billion in 2021, an increasing trend of 26.2% over the previous year [1]. However, with the tight supply of raw materials and semiconductor products, efficient chip manufacturing is critical for maintaining high product yields and avoiding costly quality issues. In general, the production process of chip manufacturing includes chip design, wafer fabrication, packaging, and testing [2]. Defects can occur in every step of this process. For example, surface breakage, scratches, air holes, and pin pressures may happen during chip packaging [3]. The deployment of defective chips into the market can lead to serious consequences, including system crashes, data loss, privacy breaches, property damage, and infrastructure paralysis, which would be catastrophic for individuals, businesses, governments, and the global community. Given this situation, it is urgent and necessary to detect these defects accurately and in a timely manner to improve manufacturing processes and avoid costly consequences.

网址：An Auto Chip Package Surface Defect Detection Based on Deep Learning https://klqsh.com/news/view/346214

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