86--13100648229

AI Chip Soldering Defect Detection Using Deep Learning Vision Inspection

2026-07-06

Latest company case about AI Chip Soldering Defect Detection Using Deep Learning Vision Inspection
United Electronics - Chip Soldering Defect Detection
Project Background

After chip soldering, it's necessary to categorize and output the results as OK or NG. For example, unsoldered chips are categorized as half-soldered or missing solder, currently judged based on solder area.

This detection solution uses a deep learning image segmentation algorithm combined with traditional logical judgment, outputting the required information format via TCP communication.

Challenges:
  • In OK cases, the top features are similar to unsoldered features.
  • The distinction between half-soldered and missing solder has a blurred boundary influenced by human intervention.
latest company case about AI Chip Soldering Defect Detection Using Deep Learning Vision Inspection                                                              Dynamic effects of chip soldering defect detection
latest company case about AI Chip Soldering Defect Detection Using Deep Learning Vision Inspection   latest company case about AI Chip Soldering Defect Detection Using Deep Learning Vision Inspection  latest company case about AI Chip Soldering Defect Detection Using Deep Learning Vision Inspection                                                                                      OK  status                                       half-welding                                Missing weld
Chip Soldering AI Visual Inspection Deployed | Solving Boundary Defect Identification Challenges by Integrating Deep Learning Segmentation Algorithms

Yield control in electronic chip soldering processes has always been a pain point in production line quality inspection. United Electronics has implemented a composite AI-based visual inspection solution for soldering defects, overcoming the shortcomings of traditional area-based methods.

I. Existing Pain Points in Production Line Inspection

After chip soldering, quality inspection needs to distinguish between good (OK) and defective (NG) products. Defects are further subdivided into incomplete soldering and missing solder joints. Traditional solutions rely solely on the solder area for judgment, resulting in two major flaws:

  • The top appearance of good chips and the texture of unsoldered areas are highly similar, easily leading to misjudgment;
  • There are no absolute quantitative standards for incomplete soldering and missing solder joint defects, making manual judgment unclear and lacking repeatability.
II. Composite AI Inspection Technology Solution

The project abandons a single inspection logic, adopting a fusion architecture of deep learning image segmentation algorithms and traditional rule-based logic:

  • The deep learning segmentation model accurately extracts the contour and texture features of the solder area, distinguishing easily confused top surfaces of good chips from unsoldered areas;
  • Traditional logic rules are superimposed for secondary verification, quantifying the unclear boundaries of incomplete soldering and missing solder joint defects, reducing human judgment bias;
  • Equipped with a standardized TCP communication interface, inspection results can be directly connected to the production line MES/host computer, outputting customized data messages for the enterprise, achieving automated flow of quality inspection data.
III. Implementation Value of the Solution

This integrated visual inspection solution solves the industry problem of pure area judgment and the inability of a single machine vision algorithm to distinguish minute welding defects. It eliminates the subjective error of manual quality inspection, takes into account both recognition accuracy and the need for data exchange between production lines, and provides a stable and implementable automated quality inspection solution for semiconductor and automotive electronic chip welding stations.