Flip-chip solder bumps defect detection using a self-search lightweight framework

The flip-chip technology is widely used in aerospace and defense electronic systems because of its high information processing, rapid response and autonomous control. As flip-chip technology advances towards higher density and finer pitch, the mechanical reliability of chip packaging will decrease, with solder bumps defect being the most common and severe concern. To ensure the safety and availability of electronic systems in industrial production applications, it is very necessary to develop the nondestructive solder bumps detection technology with less manual intervention, lightweight architecture and high detection accuracy. In this study, a self-search lightweight framework (SLF), which integrates architecture feature search (AFS) and logic-space decoupling distillation (LDD), is developed to analyze vibration signals and identify different defects of flipchip solder bumps. AFS makes up for the three sub-vulnerabilities in existing neural architecture search, which provide a homotypic combination AFST-AFSS for SLF. LDD decouples the logic space of traditional knowledge distillation into 3-probability spaces, which learn logical feature representations with class boundary discriminability, and facilitates feature migration in the SLF. The vibration noise signal contains a large amount of state information, and the combination with artificial intelligence technology will further support the high precision, efficiency, and reliability of detection. We collected vibration signals of flip-chips through ultrasonic excitation experiments and used them as data input for the proposed method. Specially, the dual-convergence property of AFS and the semantic mapping capability of LDD in SLF have been investigated respectively with the support of this vibration signals. Furthermore, an in-depth investigation of orthogonal analysis and adaptive performance reveals that SLF, which is a combination of AFS and LDD, delivers the best lightweight detection performance compared with other popular methods, which has shown tremendous potential on deploying edge equipment with limited computing power to complete detection in real industrial environments.