Purpose

Automatic optical inspection (AOI) systems have been widely used in many fields to evaluate the qualities of products at the end of the production line. The purpose of this paper is to propose a local-to-global ensemble learning method for the AOI system based on to inspect integrated circuit (IC) solder joints defects.

Design/methodology/approach

In the proposed method, the locally statistically modeling stage and the globally ensemble learning stage are involved to tackle the inspection problem. At the former stage, the improved visual background extraction–based algorithm is used for locally statistically modeling to grasp tiny appearance differences between the IC solder joints to achieve potential defect images for the subsequent stage. At the latter stage, mean unqualified probability is introduced based on a novel ensemble learning, in which an adaptive weighted strategy is proposed for revealing different contributions of the base classifier to the inspection performance.

Findings

Experimental results demonstrate that the proposed method achieves better inspection performance with an acceptable inspection time compared with some state-of-the-art methods.

Originality/value

The approach is a promising method for IC solder joint inspection, which can simultaneously grasp the local characteristics of IC solder joints and reveal inherently global relationships between IC solder joints.

中文翻译：

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基于局部到全局集成学习的IC焊点自动光学检测系统

目的

自动光学检查（AOI）系统已广泛应用于许多领域，以评估生产线末端的产品质量。本文的目的是为AOI系统提出一种局部到全局集成学习方法，该方法基于检查集成电路（IC）焊点缺陷。

设计/方法/方法

在提出的方法中，涉及局部统计建模阶段和全局整体学习阶段以解决检查问题。在前一阶段，基于视觉背景提取的改进算法用于局部统计建模，以掌握IC焊点之间的微小外观差异，从而获得下一阶段的潜在缺陷图像。在后一阶段，基于新的集成学习引入平均不合格概率，其中提出了一种自适应加权策略，以揭示基础分类器对检查性能的不同贡献。

发现

实验结果表明，与某些最新方法相比，该方法在可接受的检查时间下可获得更好的检查性能。

创意/价值

这种方法是用于IC焊点检查的一种有前途的方法，它可以同时掌握IC焊点的局部特征并揭示IC焊点之间固有的全局关系。