Printed Circuit Boards (PCBs) are the backbone of virtually all electronic devices. However, as technology advances and PCBs become smaller and more intricate, spotting defects during assembly has become increasingly difficult. Studies shows that 75% of PCB defects are attributed to issues like open Solder Joints (35%), solder shorts (20%), and component misalignment (20%). [1]
To solve these issues, AI for PCB assembly plays an important role. Lincode’s AI Visual Inspection helps manufacturers by inspecting small components like solder joints, traces, and connectors, ensuring that assembled boards match the Bill of Materials (BOM), leading to more reliable and efficient production.
In this blog, we’ll discuss the challenges faced by PCB manufacturers and how Lincode’s AI-powered visual inspection provides a solution.
What are the causes of defects in PCBs?
Defects in Printed Circuit Boards (PCBs) can arise at various stages of the manufacturing process, and understanding these causes is necessary to prevent quality issues down the line. Below are the common factors that contribute to defects in PCBs:
1. Manufacturing Process Errors
The manufacturing process itself is one of the biggest contributors to PCB defects. Errors like improper etching, drilling issues, and misalignment can all cause defects.
According to a study by IPC (Institute for Printed Circuits), manufacturing defects account for approximately 30% of PCB failures. [2] These errors might involve incorrect trace widths, holes that are too large or small, or issues with the copper layers not sticking properly.
2. Component Placement Issues
Incorrect placement of components during assembly is another major cause of defects. In high-density, complex boards, even a slight misalignment can lead to issues like faulty connections or missing components. Mostly, all PCB defects are related to issues with component placement.
Research suggests that around 30-40% of PCB defects are related to component placement errors. These misplacements can lead to costly rework and performance issues in the final product. [3]
3. Environmental Contaminants
Environmental factors like dust, moisture, and static electricity can also cause significant defects. Moisture absorption, for example, can lead to rust or short circuits, especially when exposed to heat during soldering.
Controlling the environment is crucial to avoid these contaminants during the manufacturing process. Environmental contaminants account for nearly 25% of PCB failures. [4]
4. Material Quality and Variability
Low-quality or inconsistent materials can negatively impact PCB performance. This could be due to variations in the copper used, inconsistent laminates, or substandard soldering materials. Variability in material quality can lead to weak joints, poor conductivity, and reduced lifespan of the PCB.
A report by Electronics Manufacturing Services (EMS) shows that around 20% of PCB defects are due to material inconsistencies, such as substandard copper or laminate. [5]
5. Reflow and Soldering Issues
Improper reflow soldering temperatures are responsible for many PCB defects. Soldering issues like cold joints, solder bridges, or insufficient soldering also cause PCB defects. Reflow soldering errors often happen when the oven temperature isn’t set correctly, leading to weak or poor connections between components and the PCB.
Reflow soldering issues contribute to 15-20% of PCB defects. Soldering issues such as cold joints and solder bridges, specifically, lead to 5-10% of all manufacturing defects in PCBs. [6]
6. Design Flaws
Poor design is another major source of PCB defects. Common design mistakes like incorrect trace widths, improper spacing between components, or insufficient cooling pads for heat-sensitive components. These flaws can cause signal integrity issues, component overheating, or physical failure.
Around 15-20% of PCB defects are due to design flaws, which often involve incorrect trace widths or poor thermal management in high-power applications. [7]
Challenges in Manual PCB Inspection
Manual inspection, while useful in the past, has several limitations that make it less effective in today’s advanced PCB assembly process. Some of the common issues are:
1. Difficulty in Detecting Small Defects
- With smaller and more complex PCBs, defects like tiny soldering issues or misalignments are harder for the human eye to spot, especially as board density increases.
- Research demonstrates that human inspectors often miss 20–30% of defects across various inspection tasks including electronics assembly due to visual limitations and fatigue. [8]
2. Inconsistent Results
- Different inspectors may have different standards for what counts as a defect, causing inconsistencies in quality. This makes it hard to maintain uniformity across products.
- Inspectors’ speeds can vary up to six times, but accuracy doesn’t improve much. On average, they miss about 23% of defects, leading to inconsistent results. [9]
3. Slow Inspection Speed
- Manual inspection is slow, which can create delays in high-volume production. This impacts the overall speed of the manufacturing process and increases costs.
- The research shows that manual inspection speed is typically around 10–12 items per second, and their speed vary depends on individuals.
4. Hard to Detect Complex Defects
- Some defects, like internal cracks or environmental issues, are hard to see with the naked eye, making manual inspection insufficient for identifying all potential problems.
- These defects can often go unnoticed during traditional inspections, leading to potential failures or safety risks down the line.
What are the defects in PCB are identified by Lincode’s AI Visual Inspection?
Lincode’s AI-powered visual inspection system efficiently detects a variety of defects in PCBs, ensuring high-quality and reliable manufacturing.
1.Soldering Defect
Lincode’s AI can detect common soldering defects like cold joints, solder bridges, and insufficient soldering, ensuring that all connections are strong and reliable for proper circuit function.
2. Reflow Issues
The system identifies reflow soldering issues, such as incomplete soldering or improper bonding, preventing weak joints that could lead to PCB failure during operation.
3. Misaligned SMD Components
Lincode’s AI detects misaligned Surface-Mount Devices (SMD), ensuring that all components are positioned correctly, which is crucial for proper electrical connections and functionality.
4. Foreign Particles
The AI can spot foreign particles like dust or debris on the PCB’s surface, which could potentially cause shorts or affect the board’s performance, ensuring a clean assembly.
5. Broken Trace
Lincode’s system can identify broken traces, which are critical for maintaining proper circuit flow. Detecting and repairing these issues early helps avoid malfunctioning PCBs.
How Lincode Improves PCB Quality with AI-Powered Inspection?
- Built for PCB Manufacturing: LIVIS is tailored for PCB production, detecting issues like soldering defects, misaligned SMDs, broken traces, and BOM mismatches with high accuracy ideal for complex, high-density boards.
- High Detection Accuracy: With deep learning algorithms, LIVIS detects defects with up to 90% accuracy, reducing false positives and catching errors manual inspection often misses.
- Quick Deployment: LIVIS comes with pre-trained AI models, making it easy to deploy without requiring advanced programming or long setup times.
- BOM & Label Verification: It uses OCR to validate labels, barcodes, logos, and power ratings, and cross-checks component placement with the Bill of Materials, avoiding costly mismatches.
- Real-Time Inspection: LIVIS integrates into production lines, providing instant defect alerts that help prevent downtime and rework delays.
- Cost-Efficient Operations: By reducing scrap, rework, and manual labor, LIVIS lowers production costs while improving output and product quality.
Lincode LIVIS vs Manual Inspection vs Traditional AOI
To truly understand the value Lincode brings to PCB manufacturing, it helps to see how it stacks up against manual inspection and traditional AOI (Automated Optical Inspection) systems. The table below highlights the key differences in accuracy, speed, adaptability, and cost-efficiency.
| Feature | Manual Inspection | Traditional AOI | Lincode LIVIS (AI Inspection) |
| Accuracy in Detecting Micro Defects | Low (20–30% missed) | Moderate | High (Up to 90% accuracy) |
| Real-Time Feedback | No | Limited | Instant alerts with root cause insights |
| Speed of Inspection | Slow (5–6 items/sec) | Fast | Real-time, scalable across stations |
| Learning from Past Defects | No | No | Continuous learning with defect trends |
| Adaptability to New PCB Designs | Needs re-training | Requires manual setup | Auto-adaptive with minimal setup |
| Foreign Particle Detection | Hard to spot | May miss small particles | Detects dust, rust, moisture, etc. |
| Cost Over Time | High due to labour & rework | Medium hardware/software costs | Cost-effective with low false positives |
| Integration with Existing Lines | Easy, but manual | Complex setup needed | Plug & play with minimal downtime |
| Support for OCR (Label/Text Check) | Not possible | Rare | AI OCR for logos, barcodes, ratings |
How AI Inspection Improves First Pass Yield (FPY) in PCB Assembly?
First Pass Yield (FPY) is a critical quality metric in PCB manufacturing. It refers to the percentage of boards that pass inspection the first time without requiring rework or repair. A low FPY means more time, cost, and effort spent fixing issues after the fact hurting both productivity and profitability.
Lincode’s AI-powered visual inspection system (LIVIS) changes this by providing real-time, high-accuracy detection of soldering issues, misaligned components, and BOM mismatches. As a result:
- Defects are caught immediately after placement or reflow.
- The inspection process becomes more consistent and repeatable.
- Quality engineers receive instant feedback to fine-tune processes.
Outcome
This leads to fewer reworks, reduced scrap, and significantly lower overall production costs without slowing down the production.
Final Words
As PCBs become smaller, more complex, and more critical to device performance, traditional inspection methods are no longer enough. Manual inspection is too slow and error-prone, while legacy AOI systems lack the flexibility and intelligence to adapt to today’s production needs.
Lincode’s AI-powered visual inspection system LIVIS brings a new level of precision and efficiency to PCB manufacturing. From detecting micro-defects in real-time to verifying BOM accuracy, LIVIS transforms quality control from a bottleneck into a strategic advantage.
By integrating seamlessly into existing lines and continuously learning from inspection data, Lincode make manufacturers to cut defects, reduce rework, lower costs, and ship high-quality boards. Talk to a Lincode expert today and see how LIVIS can optimize your production.
FAQ
1. What is PCB inspection?
PCB inspection is the process of checking printed circuit boards for defects, such as misaligned components, soldering issues, or broken traces, to ensure they meet quality standards before use.
2. What is defect detection in AI?
Defect detection in AI involves using machine learning and computer vision to identify flaws in products, like PCBs, in real time during production.
3. What is PCB in automation?
PCB in automation refers to using automated systems to manufacture and inspect PCBs, improving efficiency and precision and reducing human error in the production process.
4. What is the process of AOI inspection?
AOI (Automated Optical Inspection) uses high-resolution cameras to capture images of PCBs and detect defects like misalignment or soldering issues by comparing them to design files and providing real-time feedback for corrections.
5. What is the difference between manual and automated inspection?
Manual inspection relies on human operators to check for defects, which can be slow and error-prone, while automated inspection uses machines to detect defects quickly and accurately, improving speed and consistency.
Citation:
[1] PCB Trace Technologies Inc, Article, 29 July 2021
[2] IPC (Institute for Printed Circuits), Journal, Study on Manufacturing Process Errors – Published in 2023
[3] Research on Component Placement Issues by Electronics Manufacturing Services (EMS), Research Report, Published in 2022
[4] Plutomen, Article, Published on September 16, 2024
[5] Electronics Manufacturing Services (EMS) Study, Report, 2021
[6] Soldering Issues Study, Journal Article, 2023
[7] Design Flaws Study by IPC in 2022, Research Paper, Published in 2022
[8] Visual Inspection Limitations Study by Global Electronics Research, Research Paper, 2023
[9] AI in PCB Assembly Article by Plutomen, Article, September 16, 2024
