Anomaly Detection for Solder Joints Using β-VAE

In the assembly process of printed circuit boards (PCBs), most of the errors are caused by solder joints in surface mount devices (SMDs). In the literature, traditional feature extraction-based methods require designing hand-crafted features and rely on the tiered red green blue (RGB) illumination t...

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Vydáno v:	IEEE transactions on components, packaging, and manufacturing technology (2011) Ročník 11; číslo 12; s. 2214 - 2221
Hlavní autoři:	Ulger, Furkan, Yuksel, Seniha Esen, Yilmaz, Atila
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">β -variational autoencoder (VAE) Accuracy Anomalies Anomaly detection Artificial neural networks automated optical inspection (AOI) Automatic optical inspection Circuit boards Errors Feature extraction Illumination Inspection Integrated circuits Model accuracy Representations solder joint inspection (SJI) Soldered joints Soldering Solders Surface-mounted devices Training unsupervised anomaly detection VAE
ISSN:	2156-3950, 2156-3985
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Abstract	In the assembly process of printed circuit boards (PCBs), most of the errors are caused by solder joints in surface mount devices (SMDs). In the literature, traditional feature extraction-based methods require designing hand-crafted features and rely on the tiered red green blue (RGB) illumination to detect solder joint errors, whereas the supervised convolutional neural network (CNN)-based approaches require a lot of labeled abnormal samples (defective solder joints) to achieve high accuracy. To solve the optical inspection problem in unrestricted environments with no special lighting and without the existence of error-free reference boards, we propose a new beta-variational autoencoder (beta-VAE) architecture for anomaly detection that can work on both integrated circuit (IC) and non-IC components. We show that the proposed model learns disentangled representation of data, leading to more independent features and improved latent space representations. We compare the activation and gradient-based representations that are used to characterize anomalies and observe the effect of different beta parameters on accuracy and untwining the feature representations in beta-VAE. Finally, we show that anomalies on solder joints can be detected with high accuracy via a model trained directly on normal samples without designated hardware or feature engineering.
AbstractList	In the assembly process of printed circuit boards (PCBs), most of the errors are caused by solder joints in surface mount devices (SMDs). In the literature, traditional feature extraction-based methods require designing hand-crafted features and rely on the tiered red green blue (RGB) illumination to detect solder joint errors, whereas the supervised convolutional neural network (CNN)-based approaches require a lot of labeled abnormal samples (defective solder joints) to achieve high accuracy. To solve the optical inspection problem in unrestricted environments with no special lighting and without the existence of error-free reference boards, we propose a new beta-variational autoencoder (beta-VAE) architecture for anomaly detection that can work on both integrated circuit (IC) and non-IC components. We show that the proposed model learns disentangled representation of data, leading to more independent features and improved latent space representations. We compare the activation and gradient-based representations that are used to characterize anomalies and observe the effect of different beta parameters on accuracy and untwining the feature representations in beta-VAE. Finally, we show that anomalies on solder joints can be detected with high accuracy via a model trained directly on normal samples without designated hardware or feature engineering.
Author	Ulger, Furkan Yuksel, Seniha Esen Yilmaz, Atila
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Snippet	In the assembly process of printed circuit boards (PCBs), most of the errors are caused by solder joints in surface mount devices (SMDs). In the literature,...
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Title	Anomaly Detection for Solder Joints Using β-VAE
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