Issue link: https://iconnect007.uberflip.com/i/1534953
were the only method of optically examining components, are typically used to inspect the outcome: solder joints, alignment, and the presence of voids or bridges. While valuable, these systems focus on symptoms. With- out insight into what the component looked like before it was placed, root cause analy- sis remains incomplete. For example, a void in a solder joint might appear to be a pro- cess failure when, in fact, it stems from oxi- dation or contamination on the component lead, something that could have been spotted ahead of time. is is where connecting image data across machines and inspection stages becomes a game-changer. If pre-placement image anal- ysis detects a component with suspect oxida- tion or irregular geometry, this information can be forwarded to AOI or X-ray systems downstream. ese systems can then adapt their inspection parameters, increase reso- lution in specific regions, or apply alternate detection thresholds. e result is a more context-aware inspection, improved accu- racy, and reduced false alarms. What makes this approach powerful is that it doesn't require additional hardware. e 16 SMT007 MAGAZINE I MAY 2025 key lies in analysing the visual data already generated by machines in the production line. By applying AI-based algorithms to the exist- ing bottom-side images, it becomes possible to perform defect detection and component authentication simultaneously. ese algo- rithms are trained on large datasets of com- ponent images and can learn to recognize not only physical defects but also the visual signatures of manufacturers, part types, and production lots. is means that every com- ponent can be verified not just for condition, but also for origin, helping to identify coun- terfeit or incorrect components before they are soldered to the board. e same principle can be extended to X-ray inspection. While traditional X-ray analy- sis focuses on the integrity of the solder joint, cross-sectional imaging also reveals internal structures of the component. AI algorithms can be used to analyse these internal features— such as die placement, bond wires, and lead frames—to verify whether the component conforms to its expected internal architecture. is adds another layer of authentication, one that goes beyond surface appearance to detect counterfeits or unauthorized substitutions with similar external markings but dif- ferent internals. e combination of bot- tom-side visual inspection, X-ray structural analysis, and AI-powered classification cre- ates a unified inspection pro- cess that spans the entire SMT line. Each machine becomes not just a checkpoint, but a contrib- utor to a larger data ecosystem, enhancing the decisions made downstream. When a compo- nent is flagged as suspicious at the placement stage, that insight can be used by AOI to zoom in on that specific location. X-ray can focus on the internal quality of that component or verify joint