SolVision
SolVision deployments where manufacturers catch surface flaws, missing components, misread characters and seal failures on PCBs, wafers, stamped metal, packaging, pharma, textiles. Rule based AOI fails when defects vary in shape and lighting; each entry shows the samples, AI tool and detection rate.
Product Deployments
Case Studies
AccuPick
SolVision
META-aivi
SolMotion
Plastic Buckle Clip Inspection Using AI
SolVision utilizes AI for precise inspection and defect detection in plastic buckle clips, enhancing quality control and production efficiency.
AI Inspection Solution for Semiconductor Wafers
Using SolVision’s AI Inspection Solution, minute defects such as fine scratches can be located and marked on sample images to train the AI model.
Inspecting Safety Certification Marks
Inspecting Safety Certification Marks. Solomon SolVision's AI automated detection of defective certification marks.
Visual Inspection of Semiconductor Trays
Solomon SolVision’s Feature Detection tool first learns the position of the tray, then performs OCR with more flexibility than AOI systems.
Visual Inspection of Semiconductor Carrier Trays
Automated defect detection with artificial intelligence. Visual Inspection of Semiconductor Carrier Trays.
AI Inspections for Multilayer Ceramic Capacitors (MLCC)
SolVision detects defective electrodes on passive electronic components to significantly improve overall production yield.
AI Inspection for Semiconductor Components
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.
Automating Quality Inspection of Lead Frames
As semiconductor manufacturing becomes more sophisticated, the process of creating lead frames needs to improve in accuracy and yield.
Detecting Adhesion Defects in Semiconductors
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.
Automating Semiconductor Wafer Inspections
Faulty wafers also usually have subtle defects randomly scattered on the surface, and this prevents AOI systems from setting rules for efficient inspections.
AI Inspection for Aluminum LEDs
SolVision locates and recognizes the smallest defects and production anomalies in aluminum PCBAs through image processing.
Quality Control of Wafer Dicing
Using SolVision’s Instance Segmentation tool, irregular lines and multi-drilling defects are labeled in sample images to train the AI model.
Detecting Chipping Defects in Wafer Dicing
The location, size and shape of cracks vary each time, and traditional optical inspection cannot accurately identify such unpredictable defects.
Inspecting Packaged Semiconductor Chips
The Anomaly Detection tool uses deep learning technology to teach the AI model sample images of “perfect” wafers.
AI Inspection for Aluminum LEDs
SolVision locates and recognizes the smallest defects and production anomalies in aluminum PCBAs through image processing.
Quality Control of Wafer Dicing
Using SolVision’s Instance Segmentation tool, irregular lines and multi-drilling defects are labeled in sample images to train the AI model.
Detecting Chipping Defects in Wafer Dicing
The location, size and shape of cracks vary each time, and traditional optical inspection cannot accurately identify such unpredictable defects.
Inspecting Packaged Semiconductor Chips
The Anomaly Detection tool uses deep learning technology to teach the AI model sample images of “perfect” wafers.