Use of AI to Predict Compatibility Between Solder Paste Residues and Coatings

The electronic industry has always had to adapt to many challenges, such as miniaturization, higher performance or lifespan, and product reliability. To ensure greater reliability, Printed Circuit Board Assemblies (PCBAs) need to be protected from harsh environmental conditions. That's why, conformal coatings have been used for years in the automotive, military, aerospace, and energy industries. For optimum performance of these coatings, it is advisable to clean the PCBAs before application. Market constraints in terms of cost and time are forcing manufacturers to reduce the number of steps in the process. Sometimes, the cleaning stage is not considered as an option and the use of no-clean residues is preferred. An incompatibility between the solder paste residue and the coating applied could have more adverse effects than without coating. Then, physical and chemical compatibility between the two products must be ensured. These requirements triggered a new challenge: on one hand, there are many different types of coatings on the market; on the other hand, there are numerous possibilities for solder paste formulations. The ingredients of these two products are most of the time confidential and different from one manufacturer to the other. Today, there is no simple way to determine the reliability between a solder paste and a conformal coating. It requires physical and reliability measurements that often involve lengthy and costly qualification processes involving numerous tests. For these reasons, it would be very useful to develop a simple test to predict the compatibility between a solder paste and coating. Artificial Intelligence (AI) can help to determine a model to assess the compatibility. This paper shows how AI is used to predict compatibility results between solder paste residues and coatings. A physical compatibility study is carried out to benchmark some combinations of solder pastes and coatings of the market. Residues surface energy, coating surface tension and physical application tests are used as input data for the AI calculation. Results between AI prediction and the physical tests are compared to choose the right AI model for a good prediction of the compatibilities.