The Rise Of Digital Twins In Photonics

By John Oncea, Editor

Digital twins, virtual replicas of physical objects, aid in design, monitoring, and optimization across industries, including photonics.

Julius and Vincent are two unlikely twin brothers, the result of a secret genetic experiment aimed at creating the perfect child. Julius, raised on a remote island by scientists, grows up to be physically and intellectually superior. Meanwhile, Vincent, placed in an orphanage, becomes a small-time crook. On his 35th birthday, Julius learns about ... whoop, hold on.

I’m being told that’s the plot of “Twins,” the 1988 comedy film directed by Ivan Reitman, starring Arnold Schwarzenegger and Danny DeVito and we’re here to talk about digital twins, a recent development in manufacturing and engineering that, in photonics, are becoming increasingly important for various applications. That’s something completely different, isn’t it?

So, digital twins. We’ve already looked at how they’re helping enable real-time metrology precision, but how else are they being used in photonics and optics?

For starters, they’re helping scientists run the world’s most complex experiments, according to MIT Technology Review. This includes helping deploy the James Webb Space Telescope (JWST) one million miles from Earth, in Mars’ Gale Crater, and, well, throughout the entirety of all of space, really.

But more on all of that in a bit – first, let’s dig into what a digital twin is.

Digital Twins: 1960 To Today In 362 Words

The concept of digital twins was first introduced by NASA in the 1960s, according to RF Globlanet. “The most famous example is when NASA developed a digital twin to assess and simulate conditions on board Apollo 13, allowing Mission Control to quickly adapt and modify simulations to match the conditions of the damaged spacecraft and troubleshoot strategies to bring the astronauts safely home.”

Michael Grieves, a researcher whose work focused on business and manufacturing, moved the digital twin conceptual needle forward in 2002 when he “suggested that a digital model of a product, constantly updated with information from the real world, should accompany the physical item through its development,” adds MIT Technology Review. It was eight years later, in 2010, when John Vickers, a NASA employee, coined the term digital twin as part of a technology road map report for the space agency. 

Driven by the proliferation of the Internet of Things, the increase in computing power, and the popularity of the power of the cloud, the makers of digital twins have been able “to scale their models up or down or create more clones for experimentation, without investing in obscene amounts of hardware. Now, too, digital twins can incorporate artificial intelligence and machine learning to help make sense of the deluge of data points pouring in every second,” MIT Technology Review writes.

Take the JWST digital twin we referenced earlier. Raytheon built it because it “was a no-fail mission,” according to Karen Casey, a technical director for Raytheon’s Air and Space Defense Systems business, which built the software that controls JWST’s movements and is now in charge of its flight operations.

The twin tracks 800 million data points about its real-world sibling every day, creating a real-time video that’s easier for humans to monitor than many columns of numbers. The JWST team uses the twin to monitor the observatory and predict the effects of changes like software updates. During testing, engineers use an offline copy of the twin to upload hypothetical changes and then observe the outcomes. The group also uses an offline version to train operators and troubleshoot real-life issues, which Casey refers to as anomalies without specifying their nature.

Digital Twins And Photonics: Non-Specific Examples

Digital twins play a significant role in photonics and optics, advancing innovation and optimization while helping manage performance, production, and costs. Their impact is growing and becoming a game changer in several areas, including:

• Optical communication networks: Digital twins foster the design of smart, adaptive components such as transceivers.
• Lighting systems: Building digital twins can include networks of luminaires, allowing for virtual setup and predictive maintenance.
• Optical instrumentation: Digital twins help optimize the assembly of complex geometries.
• Autonomous driving systems: LiDAR and camera digital twins enable the definition, simulation, and validation of driving experiences to improve safety.
• Imaging systems: Digital twins are particularly important in optimizing production, performance customization, and data optimization for imaging technologies. They support:
  • Managing production costs
  • Anticipating manufacturing processes
  • Optimizing the supply chain
  • Parallel design of software and hardware
  • Improving testing and speeding up time to production
  • Enhancing yield, cost, and performance through improved process control
• Exploration of architectural possibilities: Digital twins allow for better simulation of system use cases and reduction of environmental impact.
• Data optimization: As images account for a significant part of data generated today, digital twins of imaging systems help in weighting and optimizing visual data for specific use cases.
• Complex systems design: Imaging digital twins are becoming key building blocks for designing self-driving cars, AR/VR platforms, smart cities, and automated plants.

In the broader context of photonics research, digital twins enable the capture and modeling of real-world objects or processes, allowing for simulation, prediction, and optimization. This technology is particularly useful in areas such as high-resolution human capture and modeling; 3D body motion and facial expression capture and synthesis; ideo-based vital sign analysis; and medical image processing and multimodal image data fusion. They also help integrate quantum photonic modules, sound propagation capture and simulation, and wireless communication channel modeling.

As photonics continues to advance, digital twins are likely to play an increasingly crucial role in driving innovation, improving efficiency, and enabling new applications across various industries.

In the broader context of manufacturing, writes ScienceDirect, digital twins are being integrated with real-time data from sensors and process models to create autonomous and dynamic digital replicas of manufacturing processes. This approach is being applied to various photonics-related manufacturing processes, including additive manufacturing and laser-based fabrication techniques.

The combination of digital twins with artificial intelligence and machine learning is particularly promising for photonics. This integration could enable self-learning machines and first-time-right production in photonics manufacturing.

The concept of cyberphotonics is emerging as a key to the Internet of Sustainable Production, where a growing database creates the foundation for innovative process chains in the circular economy. In specific applications, such as 3D confocal microscopy, researchers are developing digital twins capable of simulating image formation, back focal plane formation, and near-field effects. These models help in understanding and optimizing complex optical systems.

As photonics continues to advance, digital twins are expected to play an increasingly important role in designing, optimizing, and managing complex optical and photonic systems across various applications, from telecommunications to imaging and manufacturing.

Digital Twins, Specifically …

JWST’s digital twin is a fascinating case study of how the technology is being used. JWST was deployed in January 2022 and needed to unfold itself in a complicated choreography. According to its engineers’ calculations, there were 344 different ways it could fail.

A sunshield the size of a tennis court had to deploy exactly right, ending up like a giant shiny kite beneath the telescope. A secondary mirror had to swing down into the perfect position, relying on three legs to hold it nearly 25 feet from the main mirror. Finally, the main mirror – consisting of 18 hexagonal pieces nestled together like a honeycomb – had to assemble itself. Three golden mirror segments had to unfold from each side of the telescope, notching their edges against the 12 already fitted together. The sequence had to go perfectly for the telescope to work as intended. “That was a scary time,” Casey told MIT Technology Review.

“Over the multiple days of choreography, engineers at Raytheon watched the events unfold as the telescope did, MIT Technology Review continues. “The telescope, beyond the moon’s orbit, was way too distant to be visible, even with powerful instruments. But the telescope was feeding data back to Earth in real time, and software near-simultaneously used that data to render a 3D video of how the process was going, as it was going. It was like watching a very nerve-racking movie.”

This 3D video became JWST’s digital twin, and Raytheon’s teams watched it tensely as each of the 344 potential problems failed to make their appearance. “At last, JWST was in its final shape and looked as it should – in space and onscreen,” notes MIT Technology Review. “The digital twin has been updating itself ever since.”

NASA used Curiosity’s digital to solve the robot’s heat issues, CERN is using them to help with detector development and more mundane tasks like monitoring cranes and ventilation systems, and the European Space Agency wants to use Earth observation data to create a digital twin of the planet itself.

In 2022, the U.S. Space Force used digital twins to plan Tetra 5, an experiment aimed at refueling satellites. Additionally, the Space Force awarded Slingshot Aerospace with a contract to develop a digital twin of space, which will show what is happening in orbit and help prepare for incidents such as collisions.

Other uses of digital twins currently being developed include an Air Force plan to send a retired plane to a university so researchers could develop a fatigue profile. This is a “kind of map of how the aircraft’s stresses, strains, and loads add up over time,” writes MIT Technology Review. “A twin, made from that map, can help identify parts that could be replaced to extend the plane’s life or to design a better plane in the future. Companies that work in both defense and science – common in the space industry in particular – thus have an advantage, in that they can port innovations from one department to another.”