In the early years of computing, the most common data types were text and numbers. During the last 20 years, we have seen an exponential increase in the use of multimedia data types such as images and videos. In 2017, over 1.2 trillion photos were taken with consumer electronics, and multimedia data accounts for half of the data generated and consumed worldwide. The growth of image data is a direct consequence of the dissemination of imaging systems in multiple domains, from medical systems to smart mobile devices, automotive to manufacturing, and aerospace to defense and security.
Miniaturization and better performance have enabled the widespread use of cameras: the camera module market is predicted to grow with a 10% CAGR in the next . Meanwhile, the number of cameras and lenses in smartphones quadrupled between 2007 and 2020. Optical module complexity has increased by 60x while costs have remained flat. , maintaining innovation rates in imaging systems while balancing performance will require disruption in imaging design. Because of multimedia data deluge combined with increasingly stringent requirements for power consumption, imaging performance, compactness, sustainability, and cost, designing an imaging system without building a digital copy is no longer enough.
In this blog post, I’ll discuss digital twin technology and how it will facilitate disruption in imaging design to pave the way to performance-customized and data-optimized imaging systems.
A digital twin is a virtual replica of a complex system to help manage performance, production, and costs. It is powered by a computer program that uses real-world data to model a product and produces digital output that mimics the physical behavior of that object.
In 2017, Gartner listed digital twins as a top 10 strategic technology trend and predicted billions of systems would have digital twins in the next decade. Since then, the digital twin market has grown exponentially at an annual CAGR exceeding 30% to approach $10B in 2021. Digital twins are used in numerous domains, from physically large projects such as buildings and aircraft production to adaptative manufacturing and engineering.
In parallel, there is a trend to scale the digital twin concept down to sub-systems or complex components that will be critical elements of larger systems. Digital twins need to be as accurate and close to reality as possible. For example, digital twins of EV batteries in electronics are used to obtain an all-encompassing representation of the battery’s variables and behavior. In chip design, virtual prototyping accelerates time to market through earlier and more rapid software development and improved communication throughout the supply chain as a response to increasing semiconductor complexity.
Following this global trend, the impact of digital twins on optics and photonics is continuously growing and becoming a game changer. Digital twins for optical communication networks foster the design of smart, adaptative components such as transceivers. General lighting systems are included in a building’s digital twin, allowing the virtual setup of a network of luminaires and its predictive maintenance. Digital twins for optical instrumentation help optimize the assembly of complex geometries. A LiDAR or a camera digital twin would enable you to define, simulate and validate driving experiences to improve the safety of autonomous driving systems.
These are just a few examples of digital twins in optics and photonics. I will now focus on how digital twins will further unleash innovations in imaging systems towards production, performance customization, and data optimization.
One of the main reasons to use digital twins in imaging is to optimize production. Imaging system digital twins support a shift-left approach to managing production costs by saving potential recall costs, anticipating manufacturing processes and optimizing the supply chain, and designing software and hardware in parallel.
Having a complete camera replica early in the production process will improve testing and speed time to production. To achieve this, companies will need to combine the expertise of lens and CMOS designers with that of imaging scientists. Building a bridge between design and manufacturing by improving process control with design outputs and reciprocally making the design more resilient by learning from process control could help improve imaging system yield, cost, and performance. This helps ensure that production costs are managed as manufacturing becomes more complex. In addition, the availability of some raw materials critical to imaging systems is decreasing — thus increasing costs. Digital twins can foster lifecycle analysis and insightful reuse of essential materials. The third shift-left relies on the parallelization of hardware manufacturing and software development. As for virtual prototyping during chip design, it will save development time by enabling co-design, co-optimization, and co-verification.
With digital twins of imaging systems, we will be better able to explore architectural possibilities, simulate system use-cases, and reduce environmental impact. We will design human-centered and environmentally sustainable optical elements through virtual worlds, such as the metaverse and omniverse, populated with virtual replicas of components and systems. A multi-physics camera digital twin will simulate its behavior in such a virtual environment; designers will avoid under- or over-design by anticipating usage scenarios. Moreover, the digital twin will take virtual pictures of its virtual environment, thus generating synthetic optical data. Since images account for a significant part of data generated today, weighting and optimizing visual data for a specific use case is of paramount importance for managing data deluge. Imaging digital twins will become key building blocks for designing complex systems, such as self-driving cars, AR/VR platforms, smart cities, and automated plants. Imaging digital twins will also open up new possibilities and applications for anyone producing or using multimedia systems.
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