Use 3D Convolutional Neural Network to Inspect Solder Ball Defects

Lin, Bing-Jhang; Tsan, Ting-Chen; Tung, Tzu-Chia; Lee, You-Hsien; Fuh, Chiou-Shann

doi:10.1007/978-3-030-04167-0_24

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…9 For example, the solder ball portion of the solder joints is represented as voxel data to obtain the condition of the solder joints using two-dimensional x-ray CT images taken from multiple directions. 10 The voxel data are input to a three-dimensional convolutional neural network and classified by the output of the network. However, in typical anomaly detection tasks, a neural network classifier has the problem of requiring both normal and anomalous samples for the training stage, and their prediction performance is unstable for unknown anomalous samples not seen in training samples.…”

Section: Related Workmentioning

confidence: 99%

Adversarial autoencoder for detecting anomalies in soldered joints on printed circuit boards

et al. 2020

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The inspection of solder joints on printed circuit boards is a difficult task because defects inside the joints cannot be observed directly. In addition, because anomalous samples are rarely obtained in a general anomaly detection situation, many methods use only normal samples in the learning phase. However, sometimes a small number of anomalous samples are available for learning. We propose a method to improve performance using a small number of anomalous samples for training in such situations. Specifically, our proposal is an anomaly detection method using an adversarial autoencoder (AAE) and Hotelling's T-squared distribution. First, the AAE learns features of the solder joint following the standard Gaussian distribution from a large number of normal samples and a small number of anomalous samples. Then, the anomaly score of a solder joint is calculated by Hotelling's T-squared method from the features learned by the AAE. Finally, anomaly detection is performed by thresholding using this anomaly score. In experiments, we show that our method performs anomaly detection with few false positives in such situations. Moreover, we confirmed that our method outperforms the conventional method using handcrafted features and a one-class support vector machine. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Related Workmentioning

confidence: 99%

Adversarial autoencoder for detecting anomalies in soldered joints on printed circuit boards

et al. 2020

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Detecting Anomalous Solder Joints in Multi-sliced PCB X-ray Images: A Deep Learning Based Approach

et al. 2023

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Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things

Zhang¹

2023

COMSIS J

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Industry 4.0 is currently the goal of many factories, promoting manufacturing factories and sustainable operation. Automated Optical Inspection (AOI) is a part of automation. Products in the production line are usually inspected visually by operators. Due to human fatigue and inconsistent standards, product inspections still have defects. In this study, the sample component assembly printed circuit board (PCB), PCB provided by the company was tested for surface components. The types of defects on the surface of the PCB include missing parts, multiple parts, and wrong parts. At present, the company is still using visual inspection by operators, the PCB surface components are more complex. In order to reduce labor costs and save the development time required for different printed circuit boards. In the proposed method, we use digital image processing, positioning correction algorithm, and deep learning YOLO for identification, and use 450 images and 10500 components of the PCB samples. The result and contribution of this paper shows the total image recognition rate is 92% and the total component recognition rate reaches 99%, and they are effective. It could use on PCB for different light, different color backplanes, and different material numbers, and the detection compatibility reaches 98%.

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Use 3D Convolutional Neural Network to Inspect Solder Ball Defects

Cited by 3 publications

References 6 publications

Adversarial autoencoder for detecting anomalies in soldered joints on printed circuit boards

Adversarial autoencoder for detecting anomalies in soldered joints on printed circuit boards

Detecting Anomalous Solder Joints in Multi-sliced PCB X-ray Images: A Deep Learning Based Approach

Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things

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