Request ConsultationSilicon photonics and co packaged optics help AI data centers move data faster while reducing power use and heat. These technologies bring optical connectivity closer to GPUs, CPUs, switches, and memory to support growing AI workloads.
What Is Silicon Photonics?
Silicon photonics is an advanced optical technology that leverages silicon-based semiconductor manufacturing processes to generate, modulate, transmit, and receive optical signals. A typical silicon photonics system can include optical waveguides, modulators, photodetectors, couplers, multiplexers, demultiplexers, and laser sources. It integrates photonic components with electronic circuits on or around a silicon platform to offer high-speed data transmission with accelerated integration, CMOS compatibility, and scalability.
Electrical vs. Optical Communication
Electrical communication leverages electrons moving through copper wires and suffers from high signal loss, resistive heating, electromagnetic interference, and distance limitations. Meanwhile, optical communication leverages photons moving through microscopic silicon waveguides, offering bandwidth, near-zero resistive heating, immunity to signal crosstalk, and stable long-distance transmission. Overall, these carry more data over longer distances while using less power than the equivalent electrical communication.
Photonic Integrated Circuits (PICs)
Photonic Integrated Circuits are the optical analogs of electronic integrated circuits. Instead of transistors and wires, these integrate lasers, modulators, waveguides, couplers, and detectors onto a single chip to process and transmit information using light, performing complex communication with exceptional speed and precision while reducing system footprint, cost, and power consumption.
Together, photonic integrated circuits and silicon photonics technologies are redefining data transfer, sensor perception, and the next generation of computing infrastructure.
How will Optical Communication Shift the AI infrastructure
- Optical interconnects help to address bandwidth challenges by offering terabit-scale bandwidth as modern AI workloads require thousands of GPUs to communicate.
- Integrating co-packaged optics near GPUs and CPUs lowers latency and maximizes data transfer between processing units and memory, thereby shifting the AI architecture.
- Silicon photonics reduces power consumption by moving data through light without resistive thermal loss and ultimately improves energy and cooling efficiency.
Why AI Data Centers Need Optical Interconnects
AI clusters are increasingly creating massive volumes of data that need to move quickly across large computing clusters. Advanced solutions like optical interconnects address limitations that traditional copper cables cannot handle, including the bandwidth, distance, and thermal limits to link thousands of GPUs in modern AI clusters and manage massive data volumes in modern AI workloads.
AI training depends on large clusters of GPUs or TPUs working together, and this distributed architecture is driving the demand for high-bandwidth, low-latency interconnects to support synchronization and data exchange between compute nodes. Optical interconnects fulfil this demand, as traditional copper cables cannot support large-scale GPU-to-GPU communication.
Additionally, AI models and GPUs have long been seen as a necessary building block and critical limitation for AI and data transmission. Therefore, leveraging optical interconnects like silicon photonics and co-packaged optics integrates optical engines directly with switching chips t for next-generation AI data center commercial deployment.
Increasing network bandwidth also increases the importance of power consumption. AI data centers already contain large high-performance processors, switches, memory devices, and networking components that contribute to the total power and thermal load. Optical interconnects help to reduce the electrical transmission distance between high-speed components.
What Is Co-Packaged Optics (CPO)?
Co-Packaged Optics is an optical interconnect architecture that integrates optical engines into the same package or package-level platform as high-performance electrical Application-Specific Integrated Circuits (ASICs), shortening the high-speed electrical path. It addresses growing challenges around bandwidth density, communication latency, copper reach, and power efficiency in data-hungry networks by bringing key communication-closer elements together, namely optics and electronics.
An optical engine is an integrated photonic device that converts electrical signals to light (electrical-to-optical) and light to electrical signals (optical-to-electrical). These are considered building blocks of CPO systems. Depending on the architecture, an optical engine comprises lasers, modulators, photodetectors, drivers, trans impedance amplifiers, optical coupling structures, and silicon photonics components.
Co-packaged switches are shipping and named platforms with firm availability windows. Co-Packaged Optics places optical and electronic components close together, mitigating energy loss and lowering power requirements compared to traditional setups. Direct integration with ASICs enhances signal integrity connect multiple nodes at high bandwidths, supporting increased data processing and storage demands.
CPO vs pluggable optics
In conventional switch architectures, pluggable optical transceivers are on the front panel of switches, causing data to travel from the switch ASIC through several centimeters of copper before reaching the transceiver. At higher data rates beyond 400G, this introduces significant loss and heat, which weakens the signal and requires more power-demanding retimers and amplifiers. As bandwidth demands reach terabit levels, this begins to break down and drives demand for the CPO, which places components closer to the switching chips.
Silicon Photonics vs Traditional Copper
Silicon photonics leverages light waves to transmit data at high speeds by providing an ideal alternative to traditional copper cables as data rates scale past 100 Gbps.
| Parameters | Silicon Photonics | Traditional Copper |
| Bandwidth | It leverages Wavelength Division Multiplexing (WDM), supporting terabit speeds (800G, 1.6T, and beyond. | In this bandwidth is limited by physical resistance, skin effect, and frequency-dependent attenuation. |
| Power efficiency | It reduces energy consumption as photons do not generate resistance-based heat. | It requires significantly more power at high frequencies for heavy amplification equalization and error correction. |
| Transmission distance | It maintains signal integrity and near-zero energy loss over hundreds of meters without intermediate repeaters. | It faces rapid signal attenuation and crosstalk over distance, limiting high-speed links to short rack-level distances. |
| Latency | It enables faster, low-latency data transmission with reduced complex electrical-to-optical conversion. | Its higher latency can result from retiming and signal compensation. |
| Thermal challenges | It generates zero-resistance heat in the optical path but still requires thermal control for lasers. | It generates more heat due to electrical resistance and increasing cooling demands. |
Why HBM, GPUs, and Networking Are Connected
HBM, GPUs, and Networking are highly connected, as modern AI workloads necessitate massive and uninterrupted data flow to avoid bottlenecks in compute cores.
- AI accelerators: AI accelerators and GPUs execute the heavy parallel matrix math needed to train and run large language modelsHBM: HBM is a 3D-stacked DRAM technology that delivers significantly higher bandwidth and energy efficiency by connecting it to the GPU through a silicon interposer using through-silicon vias (TSVs), with placement at ultra-short paths for low latency and high power efficiency.
- Network Switches: While HBM feeds a single chip, modern AI models are too massive to fit on one GPU and must span thousands of chips. High-speed switches and fabrics manage high-speed, all-to-all GPU communication across distributed clusters.
- Optical Connectivity: As server clusters expand across rows, rooms, and entire data centers, traditional copper cables suffer from high signal loss and power limits. Light-based speed leverages light to transport data over longer distances at a lower power-per-bit by keeping distant GPUs synchronized.
- System-level integration: A modern AI infrastructure combines CPUs, HBM switches, and optical networks into a connected architecture. Advanced packaging technologies such as TSMC's CoWoS further integrate processors and HBM for efficient data movement.
- This architecture is becoming increasingly important as AI demands more transistor counts and higher memory bandwidth. Chip performance therefore depends on GPU compute capability, with the management of data, power, and heat across the components within the package.
Companies Driving the Technology
NVIDIA
NVIDIA’s co-packaged optics switches with integrated silicon photonics offer the most advanced networking solution for AI. These innovations achieve 5x better power efficiency and higher resiliency, accelerating AI application runtime by replacing pluggable transceivers. The Spectrum-X (Ethernet) and Quantum-X (InfiniBand) switches also accelerate manageability and provide the future of million-GPU AI factories, supported by significant investments to encourage their 2027 Rubin platform.
Broadcom
It is considered a leader in merchant switch silicon. Its Baili platform integrates 51.2 Tbps switch ASICs with optical engines by leveraging wafer-scale bonding, with 65% power reduction compared to traditional links, and opens the door for 200T AI clusters.
Marvell
Marvell improves CPO with custom silicon and strategic acquisitions by clustering AI compute silicon and optical engines on a single substrate leveraging advanced packaging, reinforced by its December 2025, USD 3.25 billion acquisition of Celestial AI for foundational optical interconnect IP.
Intel
North America excels in silicon photonics, with significant investments from major tech companies like Intel, that majorly emphasize vertical integration and its Optical Compute Interconnect (OCI) chiplets, co-packaging optics with CPUs, GPUs, and accelerators for efficient data-center applications.
TSMC
As GPU designs demand denser connectivity and faster data rates, optical transmission is becoming increasingly important. By integrating electrical and optical dies, TSMC's COUPE silicon photonics platform is set for volume production in 2026, enabling CPO with advanced 3D packaging technologies. This approach achieves 10–20x lower latency and 5–10x energy efficiency, providing the foundation for NVIDIA's CPO switches.
GlobalFoundries
This specialized foundry prioritizes optimized CMOS silicon photonics production over competing on advanced logic nodes. It manufactures PICs to support next-generation optical I/O startups and heterogeneous CPO architectures.
Coherent
Coherent provides high-density 2D VCSEL arrays and external laser sources, essential since silicon cannot emit light, as a vital supplier for CPO. In March 2026, NVIDIA announced two separate $2 billion strategic partnerships with Coherent and Lumentum. These partnerships, particularly with NVIDIA, strengthened the supply chain position.
Ayar Labs
Ayar Labs provides high-bandwidth, energy-efficient solutions specializing in optical I/O for AI scale-up. Their TeraPHY optical engine replaces copper connections directly within computer chips. With USD 650 million in funding and partnerships with TSMC, GlobalFoundries, and NVIDIA, Ayar Labs with the goal to reduce the complexities of copper interconnects.
The CPO Supply Chain
Silicon photonics
This focuses on manufacturing photonic integrated circuits leveraging standard CMOS fabrication lines such as silicon-on-insulator wafers to develop microscopic waveguides, modulators, and photodetectors.
- Key Players: GlobalFoundries, TSMC, Ayar Labs, and Intel.
Lasers
This provides the optical wavelengths either integrated closely or kept as pluggable external modules like PLS or ELS for field serviceability.
- Key Players: Lumentum, Coherent, and Broadcom.
Optical engines
This involves integration of the SiPh chip, drivers, TIAs, and optical coupling elements into a subassembly that performs electrical-to-optical conversion next to the host ASIC or GPU.
- Key Players: Broadcom, Marvell, and Cisco.
Packaging
This involves heterogeneous integration, fan-out wafer-level packaging, thermal management, and co-packaging the optical engine millimeters away from high-power switch ASICs or XPUs.
- Key Players: TSMC, ASE Group, and Amkor.
Testing
This focuses on the requirement of high-precision automated optical coupling, six-axis active alignment, and wafer-level optical or JEDEC stress testing before and after packaging.
- Key Players: GMT Global, FormFactor, and Teradyne.
Fiber optics
This involves supplying high-precision fiber array units (FAUs) and single-mode fiber interfaces to route light directly from the package edge to the system front panel.
- Key Players: U.S. Conec, Corning, and Furukawa.
Challenges to Mass Adoption
Manufacturing Yield
Larger system-on-package (SoP) and multi-chiplet designs increase silicon area, driving defect risks and threatening yield. Shrinking transistors below 3nm leads to variations that result in inconsistent performance across the wafer.
Thermal Management
High transistor density results in significant localized heat zones. Standard TIMs struggle to transfer heat effectively from stacked dies and lead to thermal throttling. Various materials expand at various rates, resulting in structural stress and delamination
Packaging Complexity
High-density substrates necessitate precision manufacturing, which is difficult to scale. Additionally, warpage and stress complicate assembly, and precise micro-bump alignment slows assembly.
Testing Challenges
Limited Access to the Inner layers of stacked 3D ICs makes fault diagnostics difficult. High-speed I/O testing is also driving up prices.
Simulating thermal workloads also challenges damage to uncooled dies.
Reliability and Standards
Interface standardization across foundries remains a challenge. However, long-term degradation limits early failures in critical sectors, and sourcing from multiple vendors also complicates reliability and warranty issues for packaging houses.
Silicon Photonics and the Future of AI Data Centers
Silicon photonics is becoming increasingly important as AI Data Centers shift towards higher-speed connectivity and larger computing systems. The transition from 800G to 1.6T optical modules and eventually 3.2T connectivity is further with the handling increasing traffic between GPUs, switches, and other components.
Concurrently, AI scale-up and rack-scale computing are bringing efficient processors and memory into tightly connected systems, increasing the need for high-bandwidth and low-power interconnects. These advanced developments not only strengthen optical interconnects but also encourage the next generation of AI infrastructure.
Future Outlook
The future of AI data center connectivity is shifting toward the higher integration of computing and optical technologies. CPO adoption is gaining attention as higher bandwidth requirements make conventional pluggable designs harder to scale, while optical I/O can also bring photonic connectivity closer to CPUs and GPUs for faster chip-to-chip optical links, helping address increasing bandwidth, latency, and power consumption demands.
Continued investment in AI infrastructure is also supporting development of these technologies, although thermal management, manufacturing, standardization, and system integration remain important hurdles.
About the Authors
Aditi Shivarkar
Aditi, Vice President at Precedence Research, brings over 15 years of expertise at the intersection of technology, innovation, and strategic market intelligence. A visionary leader, she excels in transforming complex data into actionable insights that empower businesses to thrive in dynamic markets. Her leadership combines analytical precision with forward-thinking strategy, driving measurable growth, competitive advantage, and lasting impact across industries.
Aman Singh
Aman Singh with over 13 years of progressive expertise at the intersection of technology, innovation, and strategic market intelligence, Aman Singh stands as a leading authority in global research and consulting. Renowned for his ability to decode complex technological transformations, he provides forward-looking insights that drive strategic decision-making. At Precedence Research, Aman leads a global team of analysts, fostering a culture of research excellence, analytical precision, and visionary thinking.
Piyush Pawar
Piyush Pawar brings over a decade of experience as Senior Manager, Sales & Business Growth, acting as the essential liaison between clients and our research authors. He translates sophisticated insights into practical strategies, ensuring client objectives are met with precision. Piyush’s expertise in market dynamics, relationship management, and strategic execution enables organizations to leverage intelligence effectively, achieving operational excellence, innovation, and sustained growth.