As artificial intelligence becomes more accessible and industries continue developing feasible use cases, some have explored what might happen if AI designs optics. Could the technology reduce time-consuming steps or improve the overall results? Researchers are exploring the possibilities of AI in optical design, often finding interesting outcomes.
Discovering How AI Designs Optics More Efficiently
The typical manual and time-consuming design processes for devices with scattering properties — such as amplifiers and transducers — require experienced scientists to determine the most effective options. However, the problem with that method is those involved do not know if they have found the simplest approach.
However, researchers have addressed that issue with a discovery algorithm. It performs an automated search to find options to achieve the desired scattering behavior with minimal resources. Although the group sees initial opportunities to apply their technology when developing scattering setups for photonics, microwaves and optomechanics, they identified potential future use cases for electronic devices, sensing applications and periodic structures.
Those involved with the research clarified that since there is no standard procedure designers can use to identify the best designs, they may miss out on more straightforward possibilities that would be easier to implement. This algorithmic approach drastically accelerates the search by automatically eliminating unsuitable options.
When the researchers tested this AI application to design an isolator, it quickly found the most efficient solution for a simple setup. Engineers had already discovered that design with conventional efforts, but tests showed the tool found previously unknown design options for more complex cases. The group also demonstrated how to apply the suggested designs to real-life hardware across a broad range of wavelengths.
Applying AI in optical design could ensure designers get the best results with fewer assumptions. This aspect is particularly important as devices get smaller because they achieve power and quality control with high-precision components that maintain alignment. AI-based suggestions may help them choose the right size and type of parts.
Applying AI in Optical Design for Material-Related Guidance
Large language models underpinning tools such as ChatGPT have gotten significant attention lately because they have made AI accessible to the masses, encouraging the public to interact with it and learn more about possible use cases. One is OptoGPT — an algorithm that creates versatile designs for optical multilayer film structures, which are important for products such as semiconductors, telescopes and advanced windows.
This tool produces designs in only 0.1 seconds, giving users almost instantaneous results. Additionally, the suggestions contain an average of six fewer layers than previous models provided. This improvement means it should be easier to manufacture the designs, making them even more appealing.
Designing multilayer film structures with traditional methods is a daunting task that requires extensive training and expertise. In addition to selecting the best materials, those involved must determine the optimal thickness for each layer. However, these researchers overcame those challenges by tailoring a transformer architecture for their needs, making it give users results related to specific desirable properties, such as high reflection. They created OptoGPT to respond well to various general optical design tasks people may encounter within their field.
Additionally, the group used local optimization to tweak the performance for specific tasks, such as making a high-efficiency coating for a radiative cooling application. Such fine-tuning can make the output more relevant. Engineering professionals who read industry-specific magazines often use these resources to stay up to date on the latest developments. This example shows some of the fascinating results.
Supporting Optical Improvements Through AI Investments
One reason why applications of AI in optical design have become much-discussed topics is that broader technological investment in GPUs for artificial intelligence processing has had a related positive effect on optical research. One researcher and his colleagues focused on how faster GPUs have significantly accelerated the complex simulations people use when designing optical devices.
The previous approach involved designers creating a circuit and testing its functionality by running one or two simulations. Those would take days, but this step was necessary to determine the likelihood of the circuit functioning as expected. However, advanced GPU-powered simulations work in about 10 minutes, allowing users to test and tweak their designs repeatedly.
The researchers exploring this matter are also eager to use these powerful GPUs to optimize device designs automatically, giving designers more time for rapid iteration and allowing them to reduce their dependence on intuition. However, even if AI designs optics this way, humans will remain important parts of the progress as they navigate new, more efficient workflows.
Furthering AI in Optical Design
These exciting examples show numerous possibilities for saving time, getting better results and allowing designers to be less dependent on intuition. AI tools could also improve outcomes early in their careers, helping them find innovative, highly effective solutions.
