SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence

TL;DR

This paper introduces SolderNet, an explainable AI system designed to improve the efficiency and trustworthiness of solder joint defect inspection in electronics manufacturing, addressing the limitations of manual inspection.

Contribution

The paper presents SolderNet, a novel explainable deep learning system specifically tailored for visual inspection of solder joints, emphasizing trust and transparency in manufacturing quality control.

Findings

SolderNet achieves promising accuracy in defect detection.

The system enhances transparency through explainability features.

Progress made towards deploying trustworthy AI in manufacturing.

Abstract

In electronics manufacturing, solder joint defects are a common problem affecting a variety of printed circuit board components. To identify and correct solder joint defects, the solder joints on a circuit board are typically inspected manually by trained human inspectors, which is a very time-consuming and error-prone process. To improve both inspection efficiency and accuracy, in this work we describe an explainable deep learning-based visual quality inspection system tailored for visual inspection of solder joints in electronics manufacturing environments. At the core of this system is an explainable solder joint defect identification system called SolderNet which we design and implement with trust and transparency in mind. While several challenges remain before the full system can be developed and deployed, this study presents important progress towards trustworthy visual inspection of solder joints in electronics manufacturing.