If you’ve ever repaired a motherboard or worked on a custom PCB, you already know how frustrating fault tracing can become. During my fourth semester in embedded engineering, I spent countless hours troubleshooting PCBs using traditional tools like multimeters and oscilloscopes. They work well, but once you start dealing with compact boards and tiny ICs, the process becomes slow, repetitive, and sometimes very inaccurate.
One mistake while probing pins can create even more problems on the board.
That frustration pushed me toward building something different — a smarter diagnostic tool capable of analyzing IC behavior without relying only on manual electrical probing. This project is currently called the Advanced IC Detector, and it combines computer vision, magnetic field sensing, and embedded logic to make PCB diagnostics faster and safer.
The Main Idea Behind the Project
Traditional troubleshooting methods depend heavily on voltage checks, continuity testing, and manual observation. The problem is that many IC failures are internal and difficult to detect through standard probing alone.
I wanted to explore another approach.
Instead of only checking electrical contact points, this system attempts to analyze both:
- the physical identity of the chip
- and its electromagnetic activity while operating
The goal is to help technicians quickly identify suspicious or damaged ICs without touching every pin individually.
Using ESP32-CAM for Automatic IC Identification
One of the first features I added was an ESP32-CAM module.
At first, I was manually checking chip serial numbers and searching for datasheets online during testing. That process wasted a lot of time, especially on densely packed boards where text is difficult to read.
The ESP32-CAM changed that completely.
The camera is used to visually recognize the IC and help identify its package type, orientation, and pin configuration before testing even begins.
This helps reduce mistakes like:
- probing the wrong pins
- reversing orientation
- confusing similar-looking ICs
- damaging components during troubleshooting
In future versions, I plan to connect this system with a small onboard database so datasheets and pinouts can appear automatically on-screen.
(Add a real image of your ESP32-CAM setup here)
Detecting Magnetic Flux with TMR Sensors
This is the most experimental and exciting part of the project.
Most active ICs generate tiny electromagnetic signatures while operating. These signals are invisible, but they still carry useful information about chip behavior.
To capture these changes, I started experimenting with a Tunnel Magnetoresistance (TMR) sensor.
Instead of physically probing every connection, the sensor scans above the IC and monitors variations in magnetic flux. During early testing, I noticed that healthy chips and faulty chips often produce noticeably different magnetic patterns.
For example:
- a shorted IC behaves differently from a normal one
- unstable chips produce inconsistent readings
- inactive or dead chips show weak or abnormal signatures
The idea is still under development, but the initial results are surprisingly promising.
One of the biggest challenges right now is filtering environmental electromagnetic noise because nearby components can interfere with readings.
Building the Smart Probe Logic
The hardware alone is not enough.
The real challenge is teaching the system how to interpret sensor data properly.
Right now, most of my development time is focused on creating what I call the Smart Probe Logic. This software layer processes information coming from both the ESP32-CAM and the TMR sensor in real time.
The logic is designed to:
- compare sensor readings
- filter unwanted electromagnetic interference
- identify abnormal IC behavior
- simplify diagnostic results for the user
Instead of displaying confusing raw data, the system should eventually provide a cleaner diagnostic response like:
- Healthy
- Suspicious
- Shorted
- Inactive
- Unstable
That would make PCB diagnostics much easier for beginners and technicians alike.
Designing the 3D Structure and Mounting System
After testing the sensors manually, I quickly realized that stability matters a lot.
Even small hand movements can affect magnetic readings. Because of that, I started designing custom 3D-printed mounting structures for the detector.
The current design focuses on:
- keeping the sensor aligned properly
- maintaining camera focus
- protecting internal wiring
- improving user grip during scanning
I’m still iterating on the enclosure design, but the overall structure is becoming much more stable compared to my early prototypes.
(Add screenshots of your CAD model or 3D printed prototype here)
Why This Project Could Be Useful
PCB troubleshooting can take hours, especially when faults are hidden inside integrated circuits.
If this concept works reliably, it could help in several ways:
Faster Diagnostics
Technicians could scan boards more quickly instead of manually testing every pin.
Safer Testing
Reducing physical probing lowers the risk of accidental shorts or damaged traces.
Better Learning for Students
Beginners often struggle with complex troubleshooting methods. A visual diagnostic system could make electronics repair easier to learn.
Non-Invasive Fault Detection
Analyzing electromagnetic behavior opens possibilities for safer board inspection without direct contact.
Current Challenges
The project is still in development, and there are several technical problems I’m actively working on:
- electromagnetic noise filtering
- sensor calibration
- accurate IC recognition
- improving magnetic sensitivity
- real-time data processing speed
- compact hardware integration
Some test results are very encouraging, while others still need refinement. But that’s honestly part of the engineering process.
Final Thoughts
This project started as a personal frustration with traditional PCB troubleshooting, but it has slowly evolved into one of the most interesting embedded systems projects I’ve worked on so far.
The combination of computer vision, sensor analysis, and embedded intelligence has huge potential for future diagnostic tools.
I’m continuing to improve the Smart Probe algorithms, optimize the sensor placement, and redesign the enclosure for better usability. There’s still a long way to go, but the progress so far has been exciting.
If you work with electronics repair, embedded systems, or PCB diagnostics, I’d genuinely love to hear your thoughts on this concept.
What’s the most difficult hardware issue you’ve ever had to troubleshoot?
— Malik Hassan
.jpg)
