Revealing Hidden Defects in Electronic Components with an AI-Based Inspection Method: A Corrosion Case Study
Abstract—Corrosion on electronic component terminations during assembly can lead to the failure of electronic devices. The terminations of electronic components are susceptible to corrosion when exposed to moisture and other corrosive agents during and before the assembly process. This corrosion can cause physical damage to the terminations, resulting in poor electrical contact and possible failure of the electronic component. In this paper, we present a case study where an automotive production line utilized Cybord’s AI-based inspection system to detect and prevent contamination in the soldering terminations of electronic components. The system interfaced with the vision system of pick-and-place machines in real-time and collected bottom-side images of all components placed on printed circuit boards (PCBs).
The AI algorithm, based on a 3 billion component database, detected evidence of corrosion, mold, and other contaminants on each component and allowed the removal of poor-quality components from production. The reel was disqualified and sent to a lab for SEM-EDX analysis, which confirmed the findings of the AI algorithm, that the issue was evidence of oxidation contamination. The results of this case study demonstrate the effectiveness of using AI-based inspection in detecting and preventing contamination in electronic assembly, boosting the overall quality and reliability of the final product.
I. INTRODUCTION
CCORROSION and corrosive contamination on electronic component terminations during assembly is a major issue that can result in the failure of electronic devices [1], [2]. The terminations of electronic components are made of metal and are susceptible to corrosion when exposed to moisture and other corrosive agents during and before the assembly process [3]–[6]. This corrosion can cause physical damage to the terminations, resulting in poor electrical contact and possible failure of the electronic component. Additionally, corrosion can also cause electrical failures, resulting in poor performance or complete system failure
Fig. 1. Images of chip resistors 1K .5% 0402 -55/125 63MW with detected contamination on the soldering terminations (A), vs uncontaminated soldering terminations (B) within the same reel.
Corrosion Case Study
The presented system [12] addresses this issue by interfacing with the vision system of pick-and-place machines in real time and collecting bottom-side images of all components placed on printed circuit boards (see an example in Fig. 1) [13], [14]. An AI algorithm, based on a 3 billion component database, detects evidence of corrosion, mold, and other contaminants on each component and allows the removal of poor-quality components from production [14], [15]. This can be done by disqualifying an entire reel or by signaling the location of the defective component for automatic or manual replacement.
The IPC-A-610 standard requires chip resistors to have a fillet at the side terminations. The bottom terminations are also important for solder joint strength, however, the solder fillets of the bottom terminations are not visible for chip resistors soldered on a PCB. The bottom terminations were chosen for inspection because they are visible through the pick-and-place alignment bottom view camera and are not visible at any other stage of the assembly, such as top view inspection of the components in the tape or AOI top view inspection. Furthermore, the bottom side provides a larger area with a larger interface with the environment and the tape where corrosion is likely to reside. Additionally, if corrosion is detected at the bottom view surface, it is highly likely that it is also present at the side.
The AI algorithm presented at [12] is inspecting the bottom side of the component and its model is tuned at classifying the terminations to different classes. The classes may be pristine component terminations, defective, corroded, contaminated with mold, peeling, cracks, etc. It does not only looking for discoloration, but also for variations in light reflectance and
Understanding the causes and mechanisms of corrosion on electronic component terminations [8]–[10]during assembly is crucial for preventing it from happening and for mitigating its effects. This can be achieved by using the appropriate materials, inspection, and assembly processes [1], [11]