ISSN 1551-4897
Vol. 19, Issue 4, 2022October 01, 2022 EDT
Detection of Printed Circuit Board Defects on ENIG and ENIPIG Surface Finishes with Convolutional Neural Networks and Evaluation of Training Parameters
Detection of Printed Circuit Board Defects on ENIG and ENIPIG Surface Finishes with Convolutional Neural Networks and Evaluation of Training Parameters
Articles in Vol. 19, Issue 4, 2022
Vol. 19, Issue 4, 2022
- Sensor Systems for Extremely Harsh EnvironmentsHolger KappertSebastian SchopfererNooshin SaeidiRalf DöringSteffen ZiescheAlexander OlowinskyFalk NaumannMartin JägleMalte SpanierAnton Grabmaier
- Composite Soldering Materials Based on BiSnAg for High-Temperature Stable Solder JointsAndrej NovikovMathias Nowottnick
- Detection of Printed Circuit Board Defects on ENIG and ENIPIG Surface Finishes with Convolutional Neural Networks and Evaluation of Training ParametersAngelika HablePaul TabatabaiHannah L. LichteneggerAnton ScherrThomas KrivecDieter P. Gruber
Hable, Angelika, Paul Tabatabai, Hannah L. Lichtenegger, Anton Scherr, Thomas Krivec, and Dieter P. Gruber. 2022. “Detection of Printed Circuit Board Defects on ENIG and ENIPIG Surface Finishes with Convolutional Neural Networks and Evaluation of Training Parameters.” Journal of Microelectronics and Electronic Packaging 19 (4): 123–30. https://doi.org/10.4071/imaps.1814291.