automation Archives - EFFECT Photonics

Insights

January 10, 2024

From the Lab Boutique to High Volume Production: How to Scale Up Photonics Manufacturing

Photonics, the science and technology of generating, detecting, and manipulating light, has witnessed remarkable progress…

Photonics, the science and technology of generating, detecting, and manipulating light, has witnessed remarkable progress in recent years. From cutting-edge research in academic labs to breakthrough innovations in startups, photonics is poised to revolutionize various industries, from telecommunications to healthcare. However, despite its tremendous potential, the transition from boutique lab-scale production to high-volume manufacturing remains a significant challenge.

To overcome this hurdle, the photonics industry must draw lessons from the successful scaling of electronics manufacturing. By adopting key strategies and practices that have propelled the electronics industry into the realm of mass production, photonics can pave the way for widespread adoption and integration into our everyday lives.

Learning from Electronics Packaging

A key way to improve photonics manufacturing is to learn from electronics packaging, assembly, and testing methods that are already well-known and standardized. After all, building a new special production line is much more expensive than modifying an existing production flow.

One electronic technique essential to transfer into photonics is ball-grid array (BGA) packaging. BGA-style packaging has grown popular among electronics manufacturers over the last few decades. It places the chip connections under the chip package, allowing more efficient use of space in circuit boards, a smaller package size, and better soldering.

Figure 1: Top, bottom, and side views of a ball-grid array (BGA) style package.

Another critical technique to move into photonics is flip-chip bonding. This process is where solder bumps are deposited on the chip in the final fabrication step. The chip is flipped over and aligned with a circuit board for easier soldering.

Figure 2: Simplified steps of the flip chip bonding process.

These might be novel technologies for photonics developers who have started implementing them in the last five or ten years. However, the electronics industry embraced these technologies 20 or 30 years ago. Making these techniques more widespread will make a massive difference in photonics’ ability to scale up and become as available as electronics.

Adopting BGA-style packaging and flip-chip bonding techniques will make it easier for PICs to survive this soldering process. There is ongoing research and development worldwide, including at EFFECT Photonics, to transfer more electronics packaging methods into photonics. PICs that can handle being soldered to circuit boards allow the industry to build optical subassemblies that are more accessible to the open market and can go into trains, cars, or airplanes.

Supply Chain Optimization

Electronics manufacturers have honed the art of supply chain management to achieve cost-effective and efficient production processes. This includes strategies like just-in-time inventory management, lean manufacturing principles, and global sourcing. In contrast, the photonics industry often faces challenges related to specialized materials and components, resulting in longer lead times and higher costs.

Photonics manufacturers can learn from electronics by implementing supply chain optimization strategies. This involves diversifying sources, streamlining production workflows, and leveraging economies of scale. By fostering strategic partnerships with suppliers and embracing advanced inventory management systems, the photonics industry can overcome the hurdles that have hindered its growth.

Figure 3: Key locations of EFFECT Photonics’ global supply chain. This includes our offices but also some locations where manufacturing is outsourced. We don’t mention the locations of our off-the-shelf component suppliers.

The Advantages of Moving to a Fabless Model

Increasing the volume of photonics manufacturing is a big challenge. Some photonic chip developers manufacture their chips in-house within their fabrication facilities. This approach has some substantial advantages, giving component manufacturers complete control over their production process.

Figure 4: Simplified value chain diagram of a vertically-integrated optical transceiver developer. The developer handles the PIC design, manufacturing, and packaging.

However, if a vertically-integrated chip developer wants to scale up in volume, they must make a hefty capital expenditure (CAPEX) in more equipment and personnel. They must develop new fabrication processes as well as develop and train personnel. Fabs are not only expensive to build but to operate. Unless they can be kept at nearly full utilization, operating expenses (OPEX) also drain the facility owners’ finances.

Especially in the case of an optical transceiver market that is not as big as that of consumer electronics, it’s hard not to wonder whether that initial investment is cost-effective. For example, LightCounting estimates that 55 million optical transceivers were sold in 2021, while the International Data Corporation estimates that 1.4 billion smartphones were sold in 2021. The latter figure is 25 times larger than that of the transceiver market.

Electronics manufacturing experienced a similar problem during their 70s and 80s boom, with smaller chip start-ups facing almost insurmountable barriers to market entry because of the massive CAPEX required. Furthermore, the large-scale electronics manufacturing foundries had excess production capacity that drained their OPEX. The large-scale foundries ended up selling that excess capacity to the smaller chip developers, who became fabless. In this scenario, everyone ended up winning. The foundries serviced multiple companies and could run their facilities at total capacity, while the fabless companies could outsource manufacturing and reduce their expenditures.

This fabless model, with companies designing and selling the chips but outsourcing the manufacturing, could also be the way to go for photonics. The troubles of scaling up for photonics developers are outsourced and (from the perspective of the fabless company) become as simple as putting a purchase order in place. Furthermore, the fabless model allows photonics developers to concentrate their R&D resources on the end market. This might be the most straightforward way for photonics to move into million-scale volumes.

Takeaways

Scaling up photonics manufacturing from boutique labs to high-volume production is a pivotal step in realizing the full potential of this transformative technology. By taking a page from the electronics industry’s playbook, focusing on standardization, optimizing the supply chain, and embracing automation, the photonics industry can overcome the challenges that have impeded its growth. With concerted efforts and strategic investments, the future of photonics looks poised for a paradigm shift, bringing us closer to a world illuminated by the power of light.

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Tags: automation, BGA packaging, EFFECT Photonics, electronics, Fabless model, Flip-chip bonding, Global sourcing, innovation, Integrated circuits, Integrated Photonics, Just-in-time inventory, Lean manufacturing, Manufacturing, Optical transceiver, Photonics, PICs (Photonic Integrated Circuits), R&D, Scaling up, Semiconductor, Standardization, supply chain

Insights

July 19, 2023

Towards a Zero Touch Coherent Network

Telecommunication service providers face a critical challenge: how to incorporate affordable and compact coherent pluggables…

Telecommunication service providers face a critical challenge: how to incorporate affordable and compact coherent pluggables into their networks while ensuring optimal performance and coverage across most network links.

Automation will be pivotal in achieving affordable and sustainable networks. Software defined networks (SDNs) facilitate network function virtualization (NFV), empowering operators to implement various functions for network management and orchestration. By incorporating an artificial intelligence (AI) layer for management and orchestration with the SDN/NFV framework, operators can unlock even greater benefits, as depicted in the diagram below.

carrier-network — Figure 1: Example of a carrier network with different applications (mobile, home, office) automated via SDN control and AI management.

Nevertheless, achieving a fully automated network requires interfacing with the physical layer of the network. This requires intelligent, coherent pluggables capable of adapting to diverse network requirements.

Zero Touch Networks and the Physical Layer

Telecom and datacom providers aiming to achieve market leadership must scale their operations while efficiently and dynamically allocating existing network resources. SDNs offer a pathway to accomplish this by decoupling switching hardware from software, thereby enabling the virtualization of network functions through a centralized controller unit. This centralized management and orchestration (MANO) layer can implement network functions that switches alone cannot handle, enabling intelligent and dynamic allocation of network resources. This enhanced flexibility and optimization yield improved network outcomes for operators.

Figure 2: Comparing a traditional network approach (left) with an SDN/NFC approach (right).

However, the forthcoming 5G networks will introduce a multitude of devices, software applications, and technologies. Managing these new devices and use cases necessitates self-managed, touchless automated networks. Realizing the full potential of network automation requires the flow of sensor and control data across all OSI model layers, including the physical layer.

As networks grow larger and more complex, MANO software necessitates greater degrees of freedom and adjustability. Next-generation MANO software must optimize both the physical and network layers to achieve the best network fit. Attaining this objective demands intelligent optical equipment and components that can be diagnosed and managed remotely from the MANO layer. This is where smart pluggable transceivers with reconfigurable DSPs come into play.

The Role of Forward Error Correction

Forward error correction (FEC) implemented by DSPs serves as a crucial component in coherent communication systems. FEC enhances the tolerance of coherent links to noise, enabling longer reach and higher capacity. Thanks to FEC, coherent links can handle bit error rates that are a million times higher than those of typical direct detect links. In simpler terms, FEC algorithms allow the DSP to enhance link performance without necessitating hardware changes. This enhancement can be compared to image processing algorithms improving the quality of images produced by phone cameras.

network link with FEC — Figure 3: Simplified diagram of a network link with forward error correction (FEC). The FEC encoder adds a series of redundant bits (called overhead) to the transmitted data stream. The receiver can use this overhead to check for errors without asking the transmitter to resend the data.

When coherent transmission technology emerged, all FEC algorithms were proprietary, guarded closely by equipment and component manufacturers due to their competitive advantage. Consequently, coherent transceivers from different vendors were incompatible, and network deployment required reliance on a single vendor.

However, as data center providers pushed for deeper disaggregation in communication networks, the need for interoperability in coherent transceivers became evident, leading to the standardization of FEC algorithms. The OIF 400ZR standard for data center interconnects adopted a public algorithm called concatenated FEC (CFEC). In contrast, some 400ZR+ MSA standards employ open FEC (oFEC), which provides greater reach at the expense of additional bandwidth and energy consumption. For the longest link lengths (500+ kilometers), proprietary FECs are still necessary for 400G transmission. Nevertheless, public FEC standards have achieved interoperability for a significant portion of the 400G transceiver market.

The Promise of the Smart Transceiver

The realization of a smart coherent pluggable capable of addressing various applications—data centers, carrier networks, SDNs—relies on an equally intelligent and adaptable DSP. The DSP must be reconfigurable through software to adapt to diverse network conditions and use cases.

For instance, a smart DSP could switch between different FEC algorithms to match network performance and use case requirements. Consider the scenario of upgrading a 650-kilometer-long metro link running at 100 Gbps with open FEC to achieve a capacity of 400 Gbps. Open FEC might struggle to deliver the required link performance. However, if the DSP can be reconfigured to employ a proprietary FEC standard, the transceiver would be capable of handling this upgraded link.

	400ZR	Open ZR+	Proprietary Long Haul
Target Application	Edge data center interconnect	Metro, Regional data center interconnect	Long-Haul Carrier
Target Reach @ 400G	120km	500km	1000 km
Form Factor	QSFP-DD/OSFP	QSFP-DD/OSFP	QSFP-DD/OSFP
FEC	CFEC	oFEC	Proprietary
Standards / MSA	OIF	OpenZR+ MSA	Proprietary

Table 1: Summary of different use cases and standards for a 400G pluggable transceiver.

Reconfigurable DSPs also prove beneficial in auto-configuring links to address specific network conditions, particularly in brownfield links. For example, if the link possesses high-quality fiber, the DSP could be reconfigured to transmit at a higher baud rate. Conversely, if the fiber quality is poor, the DSP could scale down the baud rate to mitigate bit errors. Furthermore, if the smart pluggable detects a relatively short fiber length, it could reduce laser transmitter power or DSP power consumption to conserve energy.

Takeaways

Smart coherent access pluggables would greatly simplify network upgrades. The DSP to match this pluggable should be able to use different error correction levels to handle different reach and future zero-touch network requirements.

The DSP can not only introduce software corrections but also effectuate optical hardware adjustments (output power, amplifier control) to adapt to different noise scenarios. Through these adaptations, the next generation of pluggable transceivers will proficiently handle the telecom carrier and data center use cases presented to them.

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Tags: 100G ZR, adaptable, AI, artificial intelligence, automation, Coherent pluggables, cost, data layer, DSPs, FEC, forward error correction, network layer, Network management and orchestration, network requirements, OPEX, physical layer, programmability, Proprietary, reach, reconfigurable, remote, SDNs, Service Providers, smart, Smart pluggable transceivers, software defined, Software-Defined Networks, standardized, standards, telecommunication, versatile, virtualization

Insights

March 29, 2023

What DSPs Does the Cloud Edge Need?

By storing and processing data closer to the end user and reducing latency, smaller data…

By storing and processing data closer to the end user and reducing latency, smaller data centers on the network edge significantly impact how networks are designed and implemented. These benefits are causing the global market for edge data centers to explode, with PWC predicting that it will nearly triple from $4 billion in 2017 to $13.5 billion in 2024. Various trends are driving the rise of the edge cloud: 5G networks and the Internet of Things (IoT), augmented and virtual reality applications, network function virtualization, and content delivery networks.

Several of these applications require lower latencies than before, and centralized cloud computing cannot deliver those data packets quickly enough. As shown in Table 1, a data center on a town or suburb aggregation point could halve the latency compared to a centralized hyperscale data center. Enterprises with their own data center on-premises can reduce latencies by 12 to 30 times compared to hyperscale data centers.

Type of Edge		Datacenter	Location	Number of DCs per 10M people	Average Latency	Size
On-premises edge		Enterprise site	Businesses	NA	2-5 ms	1 rack max
Network (Mobile)	Tower edge	Tower	Nationwide	3000	10 ms	2 racks max
	Outer edge	Aggregation points	Town	150	30 ms	2-6 racks
	Inner edge	Core	Major city	10	40 ms	10+ racks
Regional edge		Regional	Major city	100	50 ms	100+ racks
Not edge		Hyperscale	State/national	1	60+ ms	5000+ racks

Table 1: Types of edge data centers and their characteristics. Source: STL Partners

This situation leads to hyperscale data center providers cooperating with telecom operators to install their servers in the existing carrier infrastructure. For example, Amazon Web Services (AWS) is implementing edge technology in carrier networks and company premises (e.g., AWS Wavelength, AWS Outposts). Google and Microsoft have strategies and products that are very similar. In this context, edge computing poses a few problems for telecom providers. They must manage hundreds or thousands of new nodes that will be hard to control and maintain.

These conditions mean that optical transceivers for these networks, and thus their digital signal processors (DSPs), must have flexible and low power consumption and smart features that allow them to adapt to different network conditions.

Using Adaptable Power Settings

Reducing power consumption in the cloud edge is not just about reducing the maximum power consumption of transceivers. Transceivers and DSPs must also be smart and decide whether to operate on low- or high-power mode depending on the optical link budget and fiber length. For example, if the transceiver must operate at its maximum capacity, a programmable interface can be controlled remotely to set the amplifiers at maximum power. However, if the operator uses the transceiver for just half of the maximum capacity, the transceiver can operate with lower power on the amplifiers. The transceiver uses energy more efficiently and sustainably by adapting to these circumstances.

Fiber monitoring is also an essential variable in this equation. A smart DSP could change its modulation scheme or lower the power of its semiconductor optical amplifier (SOA) if telemetry data indicates a good quality fiber. Conversely, if the fiber quality is poor, the transceiver can transmit with a more limited modulation scheme or higher power to reduce bit errors. If the smart pluggable detects that the fiber length is relatively short, the laser transmitter power or the DSP power consumption could be scaled down to save energy.

The Importance of a Co-Design Philosophy for DSPs

Transceiver developers often source their DSP, laser, and optical engine from different suppliers, so all these chips are designed separately. This setup reduces the time to market and simplifies the research and design processes, but it comes with performance and power consumption trade-offs.

In such cases, the DSP is like a Swiss army knife: a jack of all trades designed for different kinds of PIC but a master of none. Given the ever-increasing demand for capacity and the need for sustainability both as financial and social responsibility, transceiver developers increasingly need a steak knife rather than a Swiss army knife.

As we explained in a previous article about fit-for-platform DSPs, a transceiver optical engine designed on the indium phosphide platform could be designed to run at a voltage compatible with the DSP’s signal output. This way, the optimized DSP could drive the PIC directly without needing a separate analog driver, doing away with a significant power conversion overhead compared to a silicon photonics setup, as shown in the figure below.

Figure 1: Comparison between the drive power and power consumption of three different PIC + DSP pairings: standard DSP with a silicon PIC, standard DSP with an InP PIC, and an optimized DSP with an InP.

Scaling the Edge Cloud with Automation

With the rise of edge data centers, telecom providers must manage hundreds or thousands of new nodes that will take much work to control and maintain. Furthermore, providers also need a flexible network with pay-as-you-go scalability that can handle future capacity needs. Automation is vital to achieving such flexibility and scalability.

Automation potential improves further by combining artificial intelligence with the software-defined networks (SDNs) framework that virtualizes and centralizes network functions. This creates an automated and centralized management layer that can allocate resources efficiently and dynamically. For example, the AI network controller can take telemetry data from the whole network to decide where to route traffic and adjust power levels, reducing power consumption.

Figure 2: Comparing a traditional network approach (left) with an SDN/NFC approach (right).

In this context, smart digital signal processors (DSPs) and transceivers can give the AI controller more degrees of freedom to optimize the network. They could provide more telemetry to the AI controller so that it makes better decisions. The AI management layer can then remotely control programmable interfaces in the transceiver and DSP so that the optical links can adjust the varying network conditions. If you want to know more about these topics, you can read last week’s article about transceivers in the age of AI.

Takeaways

Cloud-native applications require edge data centers that handle increased traffic and lower network latency. However, their implementation came with the challenges of more data center interconnects and a massive increase in nodes to manage. Scaling edge data center networks will require greater automation and more flexible power management, and smarter DSPs and transceivers will be vital to enable these goals.

Co-design approaches can optimize the interfacing of the DSP with the optical engine, making the transceiver more power efficient. Further power consumption gains can also be achieved with smarter DSPs and transceivers that provide telemetry data to centralized AI controllers. These smart network components can then adjust their power output based on the decisions and instructions of the AI controller.

Tags: 5G, AI, artificial intelligence, Augmented Reality, automation, cloud edge, Co-Design Philosophy, data centers, Delivery Networks, DSPs, Edge Cloud, Fiber Monitoring, Hyperscale Data Center, Internet of Things, IoT, latency, Network Function Virtualization, optical transceivers, power consumption, Software-Defined Networks, Telecom Operators, Virtual Reality

Insights

March 22, 2023

Transceivers in the Age of AI

Artificial intelligence (AI) will have a significant role in making optical networks more scalable, affordable,…

Artificial intelligence (AI) will have a significant role in making optical networks more scalable, affordable, and sustainable. It can gather information from devices across the optical network to identify patterns and make decisions independently without human input. By synergizing with other technologies, such as network function virtualization (NFV), AI can become a centralized management and orchestration network layer. Such a setup can fully automate network provisioning, diagnostics, and management, as shown in the diagram below.

Figure 1: Example of a carrier network with different applications (mobile, home, office) automated via NFV control and AI management.

However, artificial intelligence and machine learning algorithms are data-hungry. To work optimally, they need information from all network layers and ever-faster data centers to process it quickly. Pluggable optical transceivers thus need to become smarter, relaying more information back to the AI central unit, and faster, enabling increased AI processing.

Faster Transceivers for the Age of AI

Optical transceivers are crucial in developing better AI systems by facilitating the rapid, reliable data transmission these systems need to do their jobs. High-speed, high-bandwidth connections are essential to interconnect data centers and supercomputers that host AI systems and allow them to analyze a massive volume of data.

In addition, optical transceivers are essential for facilitating the development of artificial intelligence-based edge computing, which entails relocating compute resources to the network’s periphery. This is essential for facilitating the quick processing of data from Internet-of-Things (IoT) devices like sensors and cameras, which helps minimize latency and increase reaction times.

400 Gbps links are becoming the standard across data center interconnects, but providers are already considering the next steps. LightCounting forecasts significant growth in the shipments of dense-wavelength division multiplexing (DWDM) ports with data rates of 600G, 800G, and beyond in the next five years. We discuss these solutions in greater detail in our article about the roadmap to 800G and beyond.

Figure 2: Shipments of high-speed DWDM ports by data rate (historical data and forecast). Source: LightCounting Optical Communications Market Forecast, April 2022.

Coherent Modules Need to Provide More Telemetry Data

Mobile networks now and in the future will consist of a massive number of devices, software applications, and technologies. Self-managed, zero-touch automated networks will be required to handle all these new devices and use cases. Realizing this full network automation requires two vital components.

Artificial intelligence and machine learning algorithms for comprehensive network automation: For instance, AI in network management can drastically cut the energy usage of future telecom networks.
Sensor and control data flow across all network model layers, including the physical layer: As networks grow in size and complexity, the management and orchestration (MANO) software needs more degrees of freedom and dials to turn.

These goals require smart optical equipment and components that provide comprehensive telemetry data about their status and the fiber they are connected to. The AI-controlled centralized management and orchestration layer can then use this data for remote management and diagnostics. We discuss this topic further in our previous article on remote provisioning, diagnostics, and management.

For example, a smart optical transceiver that fits this centralized AI-management model should relay data to the AI controller about fiber conditions. Such monitoring is not just limited to finding major faults or cuts in the fiber but also smaller degradations or delays in the fiber that stem from age, increased stress in the link due to increased traffic, and nonlinear optical effects. A transceiver that could relay all this data allows the AI controller to make better decisions about how to route traffic through the network.

A Smart Transceiver to Rule All Network Links

After relaying data to the AI management system, a smart pluggable transceiver must also switch parameters to adapt to different use cases and instructions given by the controller.

Let’s look at an example of forward error correction (FEC). FEC makes the coherent link much more tolerant to noise than a direct detect system and enables much longer reach and higher capacity. In other words, FEC algorithms allow the DSP to enhance the link performance without changing the hardware. This enhancement is analogous to imaging cameras: image processing algorithms allow the lenses inside your phone camera to produce a higher-quality image.

Figure 1 Simplified diagram of a network link with Forward Error Correction (FEC). — Figure 3: Simplified diagram of a network link with forward error correction (FEC). The FEC encoder adds redundant bits (overhead) to the transmitted data stream. The receiver can use this overhead to check for errors without asking the transmitter to resend the data.

A smart transceiver and DSP could switch among different FEC algorithms to adapt to network performance and use cases. Let’s look at the case of upgrading a long metro link of 650km running at 100 Gbps with open FEC. The operator needs to increase that link capacity to 400 Gbps, but open FEC could struggle to provide the necessary link performance. However, if the transceiver can be remotely reconfigured to use a proprietary FEC standard, the transceiver will be able to handle this upgraded link.

Reconfigurable transceivers can also be beneficial to auto-configure links to deal with specific network conditions, especially in brownfield links. Let’s return to the fiber monitoring subject we discussed in the previous section. A transceiver can change its modulation scheme or lower the power of its semiconductor optical amplifier (SOA) if telemetry data indicates a good quality fiber. Conversely, if the fiber quality is poor, the transceiver can transmit with a more limited modulation scheme or higher power to reduce bit errors. If the smart pluggable detects that the fiber length is relatively short, the laser transmitter power or the DSP power consumption could be scaled down to save energy.

Takeaways

Optical networks will need artificial intelligence and machine learning to scale more efficiently and affordably to handle the increased traffic and connected devices. Conversely, AI systems will also need faster pluggables than before to acquire data and make decisions more quickly. Pluggables that fit this new AI era must be fast, smart, and adapt to multiple use cases and conditions. They will need to scale up to speeds beyond 400G and relay monitoring data back to the AI management layer in the central office. The AI management layer can then program transceiver interfaces from this telemetry data to change parameters and optimize the network.

Tags: 800G, 800G and beyond, adaptation, affordable, AI, artificial intelligence, automation, CloudComputing, data, DataCenter, EFFECT Photonics, FEC, fiber quality, innovation, integration, laser arrays, machine learning, network conditions, network optimization, Networking, optical transceivers, photonic integration, Photonics, physical layer, programmable interface, scalable, sensor data flow, technology, Telecommunications, telemetry data, terabyte, upgrade, virtualization

Insights

February 23, 2023

What Tunable Lasers Does the Network Edge Need?

Several applications in the optical network edge would benefit from upgrading from 10G to 100G…

Several applications in the optical network edge would benefit from upgrading from 10G to 100G DWDM or from 100G grey to 100G DWDM optics:

Business Services could scale enterprise bandwidth beyond single-channel 100G links.
Fixed Access links could upgrade the uplinks of existing termination devices such as optical line terminals (OLTs) and Converged Cable Access Platforms (CCAPs) from 10G to 100G DWDM.
Mobile Midhaul benefits from a seamless upgrade of existing links from 10G to 100G DWDM.
Mobile Backhaul benefits from upgrading their linksto 100G IPoDWDM.

The 100G coherent pluggables for these applications will have very low power consumption (less than 6 Watts) and be deployed in uncontrolled environments. To enable this next generation of coherent pluggables, the next generation of tunable lasers needs enhanced optical and electronic integration, more configurability so that users can optimize their pluggable footprint and power consumption, and the leveraging of electronic ecosystems.

The Past and Future Successes of Increased Integration

Over the last decade, technological progress in tunable laser packaging and integration has matched the need for smaller footprints. In 2011, tunable lasers followed the multi-source agreement (MSA) for integrable tunable laser assemblies (ITLAs). The ITLA package measured around 30.5 mm in width and 74 mm in length. By 2015, tunable lasers were sold in the more compact Micro-ITLA form factor, which cut the original ITLA package size in half. And in 2019, laser developers (see examples here and here) announced a new Nano-ITLA form factor that reduced the size by almost half again.

Evolution of tunable laser form factors for coherent optics SM — *Figure 1: Evolution of tunable laser form factors for coherent optics (2011-2021). Figure inspired from a picture in* *Laser Focus World*.

Integration also has a major impact on power consumption, since smaller, highly-integrated lasers usually consume less power than bulkier lasers with more discrete components. Making the laser smaller and more integrated means that the light inside the chip will travel a smaller distance and therefore accumulate fewer optical losses.

Reducing the footprint of tunable lasers in the future will need even greater integration of their component parts. For example, every tunable laser needs a wavelength locker component that can stabilize the laser’s output regardless of environmental conditions such as temperature. Integrating the wavelength locker component on the laser chip instead of attaching it externally would help reduce the laser package’s footprint and power consumption.

Configurability and Optimization

Another important aspect of optimizing pluggable module footprint and power consumption is allowing transceiver developers to mix and match their transceiver building blocks.

Let’s discuss an example of such configurability. The traditional ITLA in transceivers contains the temperature control driver and power converter functions. However, the main transceiver board can usually provide these functions too.

A setup in which the main board performs these driver and converter functions would avoid the need for redundant elements in both the main board and tunable laser. Furthermore, it would give the transceiver developer more freedom to choose the power converter and driver blocks that best fit their footprint and power consumption requirements.

Such configurability will be particularly useful in the context of the new generation of 100G coherent pluggables. After, these 100G pluggables must fit tunable lasers, digital signal processors, and optical engines in a QSFP28 form factor that is slightly smaller than the QSFP-DD size used for 400G transceiver.

Looking Towards Electronics Style Packaging

The photonics production chain must be increasingly automated and standardized to save costs and increase accessibility. To achieve this goal, it is helpful to study established practices in the fields of electronics packaging, assembly, and testing.

By using BGA-style packaging or flip-chip bonding techniques that are common now in electronics packaging or passive optical fiber alignment, photonics packaging can also become more affordable and accessible. You can read more about these methods in our article about leveraging electronic ecosystems in photonics.

These kinds of packaging methods not only improve the scalability (and therefore cost) of laser production, but they can also further reduce the size of the laser.

Takeaways

Tunable lasers for coherent pluggable transceivers face the complex problem of maintaining a good enough performance while moving to smaller footprints, lower cost, and lower power consumption. Within a decade, the industry moved from the original integrable tunable laser assembly (ITLA) module to micro-ITLAs and then nano-ITLAs. Each generation had roughly half the footprint of the previous one.

However, the need for 100G coherent pluggables for the network edge imposes even tighter footprint and power consumption constraints on tunable lasers. Increased integration, more configurability of the laser and transceiver building blocks, and the leveraging of electronic will help tunable lasers get smaller and more power-efficient to enable these new application cases in edge and access networks.

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Tags: automation, cost, efficiency, energy efficient, nano ITLA, optimization, power, size, smaller, testing, wavelength locker

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