Research on particle size identification method of residue in sealed electronic devices

Residue is one of the main factors affecting the reliability of sealed electronic equipment. The traditional particle collision noise detection (PIND) method is only suitable for the detection of the presence or absence of residues, but the particle size identification of the residue is important for tracking the source of production and improving the process. In this paper, the endpoint detection algorithm based on spectrum variance is used to extract the residual pulse signal, which is beneficial to the concentration of characteristic quantities. Fisher's discriminant method was used to conduct dimensionality reduction processing and clustering analysis of characteristic parameters, and BP neural network was used to realize the classification of residual particle size, and the recognition accuracy could reach 93.75%.