Prediction of electromagnetic field patterns of optical waveguide using neural network

Deep learning approach to predict optical attenuation in additively manufactured planar waveguides

The booming demand for efficient, scalable optical networks has intensified the exploration of innovative strategies that seamlessly connect large-scale fiber networks with miniaturized photonic components. Within this context, our research introduces a neural network, specifically a convolutional neural network (CNN), as a trailblazing method for approximating the nonlinear attenuation function of centimeter-scale multimode waveguides. Informed by a ray tracing model that simulated many flexographically printed waveguide configurations, we cultivated a comprehensive dataset that laid the groundwork for rigorous CNN training. This model demonstrates remarkable adeptness in estimating optical losses due to waveguide curvature, achieving an attenuation standard deviation of 1.5 dB for test data over an attenuation range of 50 dB. Notably, the CNN model's evaluation speed, at 517 µs per waveguide, starkly contrasts the used ray tracing model that demands 5-10 min for a similar task. This substantial increase in computational efficiency accentuates the model's paramount significance, especially in scenarios mandating swift waveguide assessments, such as optical network optimization. In a subsequent study, we test the trained model on actual measurements of fabricated waveguides and its optical model. All approaches show excellent agreement in assessing the waveguide's attenuation within measurement accuracy. Our endeavors elucidate the transformative potential of machine learning in revolutionizing optical network design.

Deep learning-enhanced, open-source eigenmode expansion

We present an open-source eigenmode expansion (EME) software package entirely implemented in the Python programming language. Eigenmode expansion Python (EMEPy) utilizes artificial neural networks to reproduce electromagnetic eigenmode field profiles to accelerate the EME process by a factor of 3. EMEPy provides an intuitive scripting interface, is easily compatible with a number of other Python packages, and is useful for educators and new designers.

Leveraging AI in Photonics and Beyond

Artificial intelligence (AI) techniques have been spreading in most scientific areas and have become a heated focus in photonics research in recent years. Forward modeling and inverse design using AI can achieve high efficiency and accuracy for photonics components. With AI-assisted electronic circuit design for photonics components, more advanced photonics applications have emerged. Photonics benefit a great deal from AI, and AI, in turn, benefits from photonics by carrying out AI algorithms, such as complicated deep neural networks using photonics components that use photons rather than electrons. Beyond the photonics domain, other related research areas or topics governed by Maxwell’s equations share remarkable similarities in using the help of AI. The studies in computational electromagnetics, the design of microwave devices, as well as their various applications greatly benefit from AI. This article reviews leveraging AI in photonics modeling, simulation, and inverse design; leveraging photonics computing for implementing AI algorithms; and leveraging AI beyond photonics topics, such as microwaves and quantum-related topics.