Hybrid mb/ml techniques for automated pcb defect detection comprising an inspection module including an autoencoder trained to recognize at least one feature of interest, and a defect detection module

Abstract

Hybrid MB/ML techniques for automated printed circuit board (PCB) defect detection. In one example, a PCB inspection system implements a hybrid solution using model based (MB) and machine learning (ML) technologies to detect possible defects in a PCB via an automated image capture device and processing methodology. The processing methodology fuses features from MB and ML at the latent representation. An autoencoder can be used to learn the fused data by training a neural network to produce a reconstructed image that can be compared to an original image to generate a reconstruction error value. The system produces an output indicating detection of a defect in one or more features of interest based on the reconstruction error value transgressing a threshold value.