AI COMPUTING

Thanks to their high voltage capability, thermal robustness, and high frequency performance, wide-bandgap semiconductors, especially Silicon Carbide (SiC) and Gallium Nitride (GaN), are essential to improving the energy efficiency of AI computing infrastructure.
BOOSTING ENERGY EFFICIENCY TO MEET HIGH POWER DEMAND
As AI workloads continue to grow, data center power consumption has increased significantly. Wide-bandgap semiconductor devices such as SiC and GaN help reduce power conversion losses and improve overall energy efficiency.

In server power supply units (PSUs), SiC-based modules can achieve system efficiency above 97%, significantly reducing energy waste compared with traditional silicon-based solutions. This helps lower operating costs, improve thermal management, and support the growing power demands of modern AI data centers.
HVDC ARCHITECTURE & ULTRA-HIGH POWER DENSITY
Power industry is shifting towards 800V high voltage DC (HVDC) architectures to address low efficiency power distribution when power exceeds 200 kW. Wide bandgap devices enable next generation solid-state transformer (SST) systems that convert 13.8 kV AC directly to 800 V DC. This can eliminate multiple conversion stages, improving transmission efficiency by 5%, and reducing copper usage by 45%.
Meanwhile, the high frequency capability of GaN devices allows much smaller magnetic components, reducing overall PSU size by 30 to 50%. This is ideal for spatially constrained data centers.
REDUCED COOLING REQUIREMENTS & HIGHER SYSTEM RELIABILITY
The high thermal conductivity of SiC-approximately three times that of traditional silicon-combined with the low switching losses of GaN, helps significantly reduce heat generation. This lowers cooling requirements, improves data center PUE (Power Usage Effectiveness), and enhances long-term system reliability.

In summary, wide-bandgap semiconductors improve energy efficiency, increase power density, and strengthen overall system reliability. They have become a critical foundation for sustainable AI computing growth and one of the key technologies for overcoming the energy challenges of high-performance computing.