Changes

The synthesized images can be effectively used to train a an SMC defects classification model for a future ML-based PCB-AVI application, only if they have a similar probability distribution function with respect to the original ones. In particular, it is interesting to verify if there is a clear separation in the data. To this end, it is possible to employ the t-distributed stochastic neighbor embedding (t-SNE), which is a ML algorithm for visualization based on Stochastic Neighbor Embedding (SNE) algorithm. Since the way this algorithm operates is computationally expensive, it is highly recommended to use another dimensionality reduction technique before applying t-SNE algorithm, in order to reduce the number of dimensions to a reasonable amount. For this purpose, it is possible to employ an autoencoder (AE).

The two soldering regions are the most important parts concerning the generated images and, by using the methodology proposed in the previous section, they can be easily extracted and evaluated as well with the t-SNE algorithm. Since As the two of them soldering regions associated with the same image are also correlated for the same image, clearly even strongly correlated for ''tombstoning'' class samples, they must be concurrently compressed by the AE. Therefore, a dual-stream autoencoder, with two input/output layers, is required.