Zippelius, AndreasAndreasZippeliusStrobl, TobiasTobiasStroblSchmid, MaximilianMaximilianSchmidHermann, JosephJosephHermannHoffmann, AlwinAlwinHoffmannElger, GordonGordonElger2023-08-042023-08-042022https://publica.fraunhofer.de/handle/publica/44657610.1109/ESTC55720.2022.99394652-s2.0-85143117014Scanning Acoustic Microscopy (SAM) measurements of thermally aged LED solder joints are translated to the thermal properties of the sample as characterized by Transient Thermal Analyses (TTA) using Artificial Neural Networks in order to improve the comparability of these two measurement methods. The dataset of 1800 samples with five solder pastes and nine LED types is used to study the inter- and extrapolation abilities of the trained models with respect to differences in solders and component structure. The effect of solder joint degradation due to thermal shock cycles on the ability of the model to translate is also studied with four different aging states. The architecture used is a combination of convolutional layers with max pooling and fully connected layers.enConvolutional Neural Network (CNN)LEDMachine Learningnon-destructive testingScanning Acoustic Microscopy (SAM)Solder JointsTransient Thermal Analysis (TTA)conference paper