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FIERCE Sensors — December 8, 2025
The rapid adoption of artificial intelligence (AI) in many different aspects of our lives is placing huge demands on semiconductor manufacturing technology.
Training the large language models (LLMs) being used daily requires huge arrays of ultra-high-performance GPUs. This growth trend is also placing unprecedented demand on the energy infrastructure, as data centers are expected to consume up to 40 times more power by 2030.
With each new generation of GPU technology, the interconnections linking GPUs to other key components in an AI server must operate at increasingly higher speeds.
Today’s high-end GPUs use PCI Express 6.0 (PCIe 6.0), which supports transfer rates of up to 64 Gbps. To reach these speeds, PCIe 6.0 moves beyond binary signaling and instead uses four voltage levels to encode data.
Although manufacturing such devices is incredibly complex, some of the biggest challenges faced by leading GPU manufacturers such as Nvidia relate to testing the devices they make.
Complex boards are required that use switches to configure the different validation processes. The extreme speeds and use of multiple voltage levels means that even minor distortions introduced by these boards and switches can slow validation and delay deployment.
Electromechanical Relays: A 200-Year-Old Solution
Two different solutions are used to switch signals when testing modern semiconductors. The first, electromechanical (EM) relays, relies on a technology invented by Joseph Henry in 1835 that is still in use today.
Much like a conventional switch, two metal contacts are brought together - or separated -- to make or break a physical connection. A spring mechanism with electromagnet control allows the connection state, i.e., on or off, to be controlled electronically.
