Edge Processing Reduces Variation Across Mixed Metrology Platforms

As tolerances grow tighter, manufacturers must shift from static to dynamic process control to ensure quality. In doing so, they must overcome another hurdle — data correlation. The variations introduced by mixed metrology platforms simply aren’t acceptable at sub-micron scales. Since edge processing offers a single source of truth, it could be the solution they need.

The Data Challenge of Mixed Metrology Platforms

A mixed metrology platform aggregates data from multiple measurement tools and sensors into a single, synergistic source of truth. It empowers manufacturers to analyze the geometry of components and assemblies.

By identifying defects in wafers, integrated circuit substrates, packages, and printed circuit boards early, they enhance the accuracy of quality inspections. As a result, they help maintain yields and production floor efficiency. However, managing datasets this large and varied can be difficult.

Traditional metrology systems may use dissimilar data formats or communication protocols, complicating data correlation. Moreover, when sending heaps of raw data to the cloud, they may experience latency issues. To support the transition to dynamic process control, professionals must eliminate these problems.

Why Absolute Precision Is Nonnegotiable

Metrology is most commonly used to verify product quality. The traditional stand-alone approach requires substantial resources, making it costly to provide in-line 100% inspection. This is an issue because there is no room for error in modern manufacturing, especially as the trend toward miniaturization continues to gain popularity.

Feature sizes are shrinking to under 5 nanometers (nm), with 1 nm chips on the horizon. At this scale, metrology techniques such as optical and electrical probing may not accurately measure dimensions. This is an issue because, under tight tolerances like these, even the most minor flaws can lead to errors, meaning anything short of absolute precision results in failure.

How Edge Processing Unifies Mixed Metrology Platforms

In a smart factory, managing diverse information streams from various metrology equipment can quickly become complicated. Data variation and latency make it challenging to find a single source of truth. What if leaders could harmonize data from all their measurement tools in real time, directly at the source? Edge processing would let them.

Processing data at the network’s edge—closer to the Internet of Things (IoT) devices that generate it—supports dynamic process control, thereby enhancing efficiency and yields. Bringing analysis closer to the measurement tools lowers latency and improves accuracy.

The Core Benefits of Processing at the Edge

Bringing data processing and analysis to the factory floor can help smart factory owners reduce variation across mixed metrology platforms. There are three primary ways edge computing accomplishes this.

Real-Time Insights

When processes happen on-device or on local servers rather than in the cloud, latency is lower. This technique’s fundamental value proposition is that it enables true real-time processing and analysis, allowing for immediate feedback loops and production line adjustments.

The same is true for edge AI, which can substantially accelerate processing speeds for larger workloads. Facilities could use on-device intelligent analysis for time-sensitive insights and send less critical information to the cloud, reducing network bandwidth usage.

In smart factories, unifying data directly correlates to better business outcomes. Leaders can improve efficiency and slash waste by eliminating defects in components and assemblies. With real-time insights from the edge, they can optimize resource use and make defective products things of the past.

Enhanced Security

Some data variation is normal with mixed metrology platforms. However, not all anomalies are natural—some are the work of cybercriminal groups or lone wolf hackers who silently infiltrate systems. Their goal is often to manipulate data or measurement processes for espionage campaigns. Some simply want to wreak havoc.

Security is one of edge processing’s chief benefits. Keeping data on-device prevents bad actors from stealing data in transit. Of course, organizations must leverage cybersecurity best practices to protect IoT devices from tampering.

Quality Prediction

When standardizing data from disparate sources, edge gateways serve as translators, normalizing data from various measurement tools. This technology can help them ensure raw data is in a consistent format, even if they have a combination of legacy and modern metrology equipment.

Edge AI adds intelligent, data-driven decision-making and quality prediction capabilities to local processing by running lightweight models locally. While up-front costs may be high, decision-makers could save money by tailoring a third-party vendor’s generic model to their needs rather than building one from scratch.

The Implementation Challenges of Edge Solutions

To utilize edge processing, decision-makers must invest in local data storage and compute resources. They should also consider leveraging the cloud for some workloads. Analyzing datasets at the edge is faster, but requires more computation power. Sending noncritical data to central servers saves resources by sacrificing speed.

Research proves cloud computing offers greater computational power and superior long-term storage capabilities. In contrast, edge computing substantially reduces latency and bandwidth consumption. It is better suited to applications that require immediate processing. The core difference boils down to large-scale data management versus real-time responsiveness.

The Promise and Pitfalls of Edge AI

Traditional metrology faces speed and accuracy limitations—issues that intelligent edge processing could solve. By integrating AI at the edge, facilities can better analyze data from multiple, varied sources. For instance, combining X-ray and three-dimensional scans would improve semiconductor manufacturing. However, poor dataset quality and size complicate matters.

Algorithms may struggle to handle the diverse measurement needs across sub-micron to millimeter scales. Deploying a lightweight model on edge IoT devices addresses the performance problem, but introduces reliability issues.