Applications

SolVision’s deep learning facilitates the recognition of varying colored liquids and sedimentation levels for accurate classification and quality control.

SolVision detects defective electrodes on passive electronic components to significantly improve overall production yield.

Overcoming traditional AOI limitations, Solomon’s SolVision quickly performs OCR without being affected by background or lighting conditions, complexity, or appearance of the serial number.

Small, spiral-surfaced metal parts can be inspected using SolVision’s Instance Segmentation tool to learn the different types of cut marks or collision faults from sample images, then building an AI model to recognize these subtle defects.

As semiconductor manufacturing becomes more sophisticated, the process of creating lead frames needs to improve in accuracy and yield.

Excessive adhesive may remain on the chip or overflow on the circuit board and cause the chip to tilt, affecting stability of the whole semiconductor package.

Faulty wafers also usually have subtle defects randomly scattered on the surface, and this prevents AOI systems from setting rules for efficient inspections.

AI model can accurately identify errors in cable connections and eliminate substandard products in real time.

Powered by AI, SolVision deep learning software trains an AI model by simulating potential weld bead defects in random brightness conditions.