Forbes | How Silicon Photonics Is Tackling the AI Data Traffic Jam

Introduction

With the rapid advancement of artificial intelligence technologies, companies like OpenAI, Anthropic, and emerging players such as DeepSeek are driving innovation. However, the underlying computing infrastructure is facing major challenges. Current data transmission capabilities are struggling to keep up with growing demands. In a recent Forbes article, it explained how silicon photonics is poised to revolutionize AI infrastructure by addressing the issue of data transmission bottlenecks [1].

Figure 1 – Illustration of copper interconnects in large-scale AI systems, where thousands of processors collaborate to train models.

Challenges of Copper Wiring in Modern Computing

Today’s systems primarily rely on copper interconnects for data transmission between processors. Although copper wiring has reliably served chip manufacturing for decades, it is increasingly becoming a bottleneck in modern AI systems. These systems require thousands of processors to work in unison to train complex models and analyze massive datasets, but the bandwidth and efficiency limits of copper connections result in significant performance degradation.

This challenge extends beyond just speed limitations. Copper-based interconnects consume significantly more power, increasing the carbon footprint of AI computing. This runs counter to global sustainability goals like achieving carbon neutrality by 2050. The environmental impact is triggering stricter regulatory scrutiny, potentially hindering the growth and adoption of AI technologies across industries.

Silicon Photonics: A Breakthrough Solution

Silicon photonics technology replaces traditional copper interconnects with optical fibers, enabling data to be transmitted via light instead of electricity. According to Dr. Hesham Taha, CEO of Teramount, this shift is more than just an upgrade—it’s a necessary evolution for the future of computing. Technology offers compelling advantages: faster data transmission, lower latency, and reduced power consumption.

Technical Challenges and Solutions in Implementation

Despite its benefits, implementing silicon photonics presents unique technical hurdles. One of the most notable issues is the complex process of aligning optical fibers with silicon chips—a challenge referred to as “optical mode size mismatch.” This problem can be likened to trying to drink a milkshake with a straw that’s either too big or too small—the flow is either too strong or insufficient. Similarly, aligning fibers and chips with high precision requires advanced engineering techniques.

Companies like Teramount are addressing these challenges through innovation, such as developing the universal photonic coupler, which leverages self-aligned optical technology to simplify the manufacturing process. This advancement allows optical connectivity to be more seamlessly integrated into standard semiconductor workflows, enabling AI chip manufacturers to scale optical integration more cost-effectively.

Industry Adoption and Technological Applications

The industry is responding positively to silicon photonics. Key players like Lightmatter have secured significant funding, with their recent series D round raising $400 million and valuing the company at $4.4 billion. Semiconductor giants such as TSMC and Intel are also heavily investing in silicon photonics, recognizing its critical role in the future of AI data centers.

As AI applications continue to grow in complexity and scale, the demand for more efficient data transmission solutions will only increase. Silicon photonics is not just a temporary fix—it is a foundational technology supporting the next wave of AI innovation, enabling data processing capabilities to meet the evolving demands of artificial intelligence.