Challenge
AI clusters rely on lasers to move data between tens of thousands of GPUs. However, the InP (Indium Phosphide) lasers used in current optical modules are showing their limits. They need constant cooling, require costly isolators, and fail too often, causing downtime that interrupts AI training runs.
Opportunity
With hyperscalers such as Amazon, NVIDIA, and AMD already validating Quantum Dot (QD) lasers, industry confidence in the technology is rapidly increasing. This validation accelerates adoption in large-scale data centre infrastructure, where reliability and energy efficiency are critical. As a result, the market for QD lasers is projected to reach $2.6B by 2030.
Solution
The aim is to use Quantum Dot laser modules to overcome the reliability and energy bottlenecks of AI data centre interconnects, in order to outperform today’s industry-standard InP lasers.
Reliability and efficiency are critical in large-scale AI infrastructure. Quantum Dot lasers offer clear advantages in stability and thermal performance. This solution explores how these lasers can strengthen next-generation optical modules. It’s about improving robustness where it counts.
Benefits
This mission introduces Quantum Dot-based External Laser Source (ELS) modules. These run uncooled at high temperatures, are immune to optical feedback, and deliver far greater reliability and energy efficiency than competitor technologies.
