  Quality inspection applications in industry are required to move towards a
zero-defect manufacturing scenario, withnon-destructive inspection and
traceability of 100 % of produced parts. Developing robust fault detection and
classification modelsfrom the start-up of the lines is challenging due to the
difficulty in getting enough representative samples of the faulty patternsand
the need to manually label them. This work presents a methodology to develop a
robust inspection system, targeting thesepeculiarities, in the context of solar
cell manufacturing. The methodology is divided into two phases: In the first
phase, an anomalydetection model based on a Generative Adversarial Network
(GAN) is employed. This model enables the detection and localizationof
anomalous patterns within the solar cells from the beginning, using only
non-defective samples for training and without anymanual labeling involved. In
a second stage, as defective samples arise, the detected anomalies will be used
as automaticallygenerated annotations for the supervised training of a Fully
Convolutional Network that is capable of detecting multiple types offaults. The
experimental results using 1873 EL images of monocrystalline cells show that
(a) the anomaly detection scheme can beused to start detecting features with
very little available data, (b) the anomaly detection may serve as automatic
labeling in order totrain a supervised model, and (c) segmentation and
classification results of supervised models trained with automatic labels
arecomparable to the ones obtained from the models trained with manual labels.
