  In the assembly process of printed circuit boards (PCB), most of the errors
are caused by solder joints in Surface Mount Devices (SMD). In the literature,
traditional feature extraction based methods require designing hand-crafted
features and rely on the tiered RGB illumination to detect solder joint errors,
whereas the supervised Convolutional Neural Network (CNN) based approaches
require a lot of labelled 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 Autoencoders (beta-VAE)
architecture for anomaly detection that can work on both 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 on 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 on directly normal samples without designated
hardware or feature engineering.
