Silicon Photonics Adoption in Transceivers:
Past, Present and Future

The market for silicon photonics based transceivers continues to show robust growth, with revenue opportunities increasing across a wide range of use cases: Ethernet, WDM (Wavelength Division Multiplexing), AOC (Active Optical Cables) and more.

According to LightCounting data, sales of silicon photonics-based transceivers are expected to reach $9 billion, with WDM and Ethernet making up the 2 largest segments of demand.

   (image source: LightCounting) 

The rosy outlook for silicon photonics in transceivers is due in large part to the relative ease of fabrication, and the relative gains it can offer in terms of cost saving and performance. 

Here, we’re taking a look at these and other factors, especially the role of AI in accelerating demand for silicon photonics.

From Small to Large Scale: Silicon Photonics in Perspective
Phase 1: Small-Scale Integration (SSI)

Throughout the early 2000’s, silicon photonics components were used for basic elements like waveguides, modulators and detectors.

One of the key developments of this period was the demonstration of the feasibility of integrating optical components on silicon. This discovery led to the first silicon photonic integrated circuits (PICs).

For the time being, applications were limited to laboratory prototypes and proof-of-concept devices, largely because a number of technical challenges persisted. Challenges: These included the challenge of integrating a small number of components, high loss in waveguides, and immature fabrication processes.

Phase 2: Medium-Scale Integration (MSI)

From the late 2000’s to the 2010’s, the speed of innovation accelerated. For the first time, it became possible to integrate tens of components onto a single chip.

These years saw the Integration of Mach-Zehnder modulators (MZMs), and the development of multiplexers and demultiplexers. They also saw improvements in waveguide losses and coupling efficiencies.

Along with the growth in integration, new applications opened up for silicon photonics, including data centers and high-performance computing environments. Intensity-modulated direct-detect (IMDD) and WDM coherent transceivers were among the biggest winners in this wave.

 
Phase 3: Large-Scale Integration (LSI)

The MSI phase brought about a reduction in power consumption, in tandem with higher bandwidth capabilities. These developments set the stage for LSI. Now, it became possible to integrate hundreds of components on one chip. Advanced integration techniques also came to the fore, such as optical switches, lasers and detectors (all on the same chip).

This was good news for next-generation data centers, telecommunications, and emerging applications like quantum computing and artificial intelligence. Silicon Photonics began to show promising results in providing higher data rates, power efficiency, and functionality, enabling complex and high-density optical interconnects.

Comparative Limitations of Indium Phosphide

Indium Phosphide has lost share for the following reasons:

Material Complexity: InP devices are more complex and expensive to manufacture due to the intrinsic properties of the material and the need for specialized fabrication techniques.

Integration Challenges: Integrating a large number of components on InP has proven to be more challenging than with silicon.  InP devices are typically built on smaller wafers (2-4” are common) compared to silicon photonics (8” is common).  In addition, yields in an InP fab are typically worse than in a silicon photonics fab and less stable.  For these reasons, higher levels of integration of InP have traditionally been less achievable in a cost efficient manner. 

Heat Dissipation: InP devices tend to be more sensitive across temperature, which can affect performance and reliability.

How Has Generative AI Impacted the Adoption of Silicon Photonics in Transceivers?

The rise of generative AI has brought with it a heightened demand for high-speed data processing. Silicon photonics has proven its utility here, enabling more efficient data transfer between processors and switches. By allowing the integration of multiple optical components on a single chip, silicon photonics offers superior integration which supports the increased parallelism and throughput needed for AI training and inference, enabling data centers to handle the explosive growth generated by AI workloads. 

Transform Your Data Infrastructure with DustPhotonics

DustPhotonics uses advanced integration techniques and low-loss laser coupling technology to deliver high-performance, scalable products that meet the increasing demands of data centers and AI applications.

Contact us to learn more about our innovative solutions and how we can help you stay ahead in the fast-evolving world of optical communications.

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Ronnen Lovinger

CEO & Board Member

Python Automation Engineer­

Modiin, Israel







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