Cybord technology is based on visual Deep-AI algorithms and big data, adding a new dimension of components visual analytics to the SMT machine metadata created in the production line.
Advanced deep network that learns from diversity
Cybord developed a unique visual Deep-AI algorithm that analyzes each component based on a vast database of billions of electronic components images. This process provides accurate analysis utilizing a high-speed and efficient method.
Pick & Place Process
AOI at SMT Line
A single reel or a batch may have mixed parts within them
Cybord analyzes the external physical characteristics of all components in the reels, using Aquila for verifying all characteristics of the bottom side of the component.
In addition to the physical characteristics, Kingfisher and Osprey implement an AI marking technology to verify they have the same date code, lot code, and batch, and presents alerts on suspected findings.
The Cybord innovative mechanism has trained a Deep-AI model based on Big Data of images to identify components characteristics and distinguish between them.
The system identifies the original manufacturer and compares it to the documentation. This ensures that the intended component manufacturer is used in accordance with component documentation and AVL (Approved Vendor List).
Leads are soft metals sensitive to humidity and oxidation.
Corrosion occurs as a result of poor storage conditions and humidity. The surface of the soldering leads changes from smooth to rough with time, impacting the board’s life.
Based on the images taken, the smart Cybord algorithm is able to estimate the age level of corrosion on a component, and present alerts, to prevent corrosion and soldering issues.
Components may have cracks, dents, voids, contaminations, peel-offs, and other types of defects.
Cybord identifies defects that are on the packaging or surface of the items. Most manufacturing defects and leads defects will be found on the bottom view by Aquila, which checks all components bottom and leads, while Kingfisher and Osprey check all top issues, to ensure the material is in mint, intact condition, and presents alerts on defective components.
Identifying markings enables traceability of the components.
The Cybord Visual-AI algorithm detects and analyzes the meaning of each marking, which represents different characteristics, such as part number, manufacturer, country code, date code, lot code, for a higher level of traceability.
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