AI-Empowered Optical Solutions

Intelligent Photonics: Marrying Physics with Neural Networks

Physics-Informed Neural Networks (PINNs) offer a revolutionary approach to solving partial differential equations (PDEs) by directly embedding physical laws into the neural network's training process. This fusion enables highly efficient and accurate computations, bridging the gap between complex physical phenomena and data-driven AI.

At Bielcrystal, we have successfully implemented and applied classical algorithms within this domain, such as the Fourier Neural Operator (FNO). Our FNO models have demonstrated remarkable capability in predicting the electromagnetic response of novel optical structures with high fidelity, significantly accelerating the design and analysis cycle.

By training these AI models with data from simulations and experiments, we create powerful predictive tools that can rapidly explore design spaces, optimize parameters, and even discover new optical functionalities that might be non-intuitive through traditional methods alone.

Visualization of Fourier Neural Operator predicting the electromagnetic response of a novel optical structure.

Manufacturing Tolerance Consideration

Schematic diagram illustrating the consideration of manufacturing tolerances during an AI-driven optimization process for an optical component.

Our AI models can rapidly assess the impact of manufacturing variations, enabling robust designs optimized for real-world production, ensuring feasibility and performance.

Advantages of AI in Optical Engineering

• Cost Efficiency: Leverage and repurpose existing simulation and experimental data to train robust AI models, reducing the need for extensive new computations.
• Drastically Reduced Computation Time: AI models, once trained, can predict complex optical responses in seconds, compared to hours or days for traditional Maxwell solvers, enabling rapid design iterations.
• Inherent Parallelism for Tolerance Analysis: Neural networks are naturally suited for parallel computation, making it feasible to incorporate manufacturing tolerances directly into the optimization process, leading to more robust and manufacturable designs.
• Discovery of Novel Solutions: AI can explore vast parameter spaces and identify non-intuitive design solutions that surpass human-designed or conventionally optimized counterparts.
• Accelerated Material Discovery: Employ AI to predict optical properties of new materials or composite structures, speeding up the development of next-generation optical components.