Using scanning acoustic microscopy and LM-BP algorithm for defect inspection of micro solder bumps

Abstract Micro solder bump has been widely used in electronic packaging. Currently a number of flip-chip products are developing towards miniaturization with more I/Os at finer pitch, and defect inspection of the high density package is increasingly challenging. In this paper, the Levenberg-Marquardt back-propagation network (LM-BP) combined with the scanning acoustic microscopy technology was investigated for intelligent diagnosis of solder defect. The flip chips were detected by using a 230 MHz ultrasonic transducer. Solder bumps were segmented from the SAM image. The statistical features were extracted and fed into the LM-BP networks for bump classification. The results demonstrate that LM-BP algorithm reached a high recognition accuracy, and is effective for defect inspection of the micro solder bump.