AI-enabled inspection of optic fiber end faces is at a nascent stage, but shows great potential in defect detection
As demand for bandwidth continues to escalate, networks are under constant stress to deliver peak performance around the clock. Since 2021, the global internet bandwidth has doubled, increasing by 23 percent in 2025 alone. The ultra-high demands of AI and machine learning workloads fueling this growth, are placing unprecedented strain on the traditional network infrastructure.
To keep up, data center operators are pivoting towards high-speed 800G and 1.6Tb/s optical transceivers. In effect, the number of fiber interconnects and new multi-fiber connectivity supporting this infrastructure are growing in leaps and bounds. In this new reality, the quality of connections play a critical role in ensuring performance for optical communication networks.
Low-quality connections remain a leading cause of fiber-related downtimes and failures in data centers. The root cause is often benign — dust accumulation, contamination, and small defects. These seemingly minor imperfections have a direct impact on the network performance. For example, a compromised connector can irreplaceably damage the mating connector, impacting network performance. Same goes for microscopic scratches and bumps.
Contaminants and defects degrade connections from within. Debris present on the surface of connector end faces block the light beam or produce air gaps, obstructing physical contact, resulting in signal loss. Studies show that contamination raises risks of insertion loss, and Bit Error Rate Test results, and reduce return loss. The International Electrotechnical Commission’s cleanliness criteria prohibits presence of any scratches and defects in the fiber core.
With the introduction of new and specialized high-density connectors like the multifiber push-on (MPO) connectors that can fit multiple fibers in a single ferrule, testing becomes all the more challenging. There are more things to be mindful of as each connector comes with its own set of specs and tolerance to contamination (for example, low loss and low return loss connectors demonstrate very high sensitivity toward contaminations). But what’s trickier is reliably assessing contamination with a visual inspection. MT ferrules tend to mask oil, dents and other impurities on the surface, and traditional probe microscope being low-precision cannot be relied for these scenarios.
Embedding AI can vastly improve the detection accuracy in optical probe microscopes. Trained on very large datasets, AI models can extract precise insights by inspecting images pixel by pixel, while also iteratively improving accuracy. So when the images are too noisy and complex for classical image analysis, AI models can self-examine textural information and identify hidden scratches, pits and contaminants.
Deep learning algorithms can even perform defect segmentation, beyond the normal shape recognition and pattern detection. Studies show that compared to traditional optical microscopes, AI-based tools deliver significantly higher efficiency.
However, although AI has its perks, in many cases, it is an overkill. Experts say that AI-powered inspection is neither a necessity, nor should be used as a standalone tool for analyzing fiber optic connector end faces.
There are two brands of reasoning behind this. AI’s compute-intensive nature makes it a poor fit for field analysis where quick, power-efficient solutions have dominated for years. On-device AI inference demands even more resources and memory capacity at that.
“In many situations, AI is like using a hammer to crack a nut… and a computational resource heavy hammer at that,” wrote Eric Anderson, product line manger at Viavi, in a blog.
The second is, the high levels of accuracy delivered by AI models isn’t required for scenarios where a manual visual inspection with optical microscopes does the job.
A clever approach is to layer on AI capabilities to amplify the core features of traditional probe microscopes. This way the familiar tool continues to provide quick and reliable analytics in the field, while AI’s powerful image analysis serves in complex scenarios.