Deep learning in nano-photonics: inverse design and beyond

Abstract: Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nanostructures. Many of the recent works on machine-learning inverse design are highly specific, and the drawbacks of the respective approaches are often not immediately clear. In this review we want therefore to provide a critical review on the capabilities of deep learning for inverse design and the progress which has been made so far. We classify the different deep learning-based inverse design approaches at a higher level as well as by the context of their respective applications and critically discuss their strengths and weaknesses. While a significant part of the community's attention lies on nano-photonic inverse design, deep learning has evolved as a tool for a large variety of applications. The second part of the review will focus therefore on machine learning research in nano-photonics "beyond inverse design". This spans from physics informed neural networks for tremendous acceleration of photonics simulations, over sparse data reconstruction, imaging and "knowledge discovery" to experimental applications.

• The paper introduces DL methods, including surrogate models and tandem networks, to significantly speed up the inverse design process in nano-photonics.
• The paper employs advanced architectures like GANs and PINNs to tackle non-uniqueness and solve complex physical equations with improved accuracy.
• The paper highlights practical experimental applications, demonstrating DL's potential in real-time imaging and enhanced optical data interpretation.

Overview of "Deep learning in nano-photonics: inverse design and beyond"

The paper "Deep learning in nano-photonics: inverse design and beyond" by Peter R. Wiecha et al. provides a comprehensive review of the roles deep learning (DL) plays in the field of nano-photonics, focusing on its potential and application in inverse design and beyond. The authors critically discuss various DL techniques and their efficiencies, and they explore the broader landscape of machine learning in nano-photonics applications, offering insights into both the successes and challenges of these emerging tools.

Deep Learning for Inverse Design

The review distinguishes DL techniques for inverse design in nano-photonics into major categories, evaluating their strengths and limitations. Inverse design entails determining the physical parameters of a system to achieve a desired target, such as specific optical properties in nano-photonic structures. Conventional design methods often involve intuitive guess-and-check approaches, which may include iterative numerical simulations. However, these techniques are computationally expensive and time-consuming.

1. Surrogate Models: The paper discusses the use of DL-based surrogate models that significantly speed up the design process by approximating simulators. These models, once trained, can predict the optical response of photonic structures with remarkable speed compared to conventional simulations. Despite their speed, these surrogate models are approximations and may introduce system-specific errors.
2. Direct Inverse Solutions: DL architectures such as tandem networks, generative adversarial networks (GANs), and autoencoders have been employed to address non-uniqueness in inverse problems. Each method offers unique advantages; for example, GANs can manage one-to-many problems by encoding potential solutions in latent variables.
3. Improving Inverse Design Accuracy: The authors explore methods to improve the accuracy of DL predictions in inverse design, such as iterative training data enhancement, where the models learn from errors across multiple iterations.

Beyond Inverse Design

The authors also explore the application of DL in nano-photonics beyond inverse design:

1. Physics-Informed Neural Networks (PINNs): These are used to solve partial differential equations directly, augmenting the understanding of the underlying physics without the need for labeled datasets. PINNs stand out for their potential to handle complex systems with embedded physics knowledge.
2. Knowledge Discovery and Interpretation: DL models are employed to extract and interpret physical properties from large datasets. By examining the latent spaces of ANNs, researchers can gain insights into the impact of structural variations on optical responses.
3. Experimental Applications: The review highlights DL applications in analyzing experimental data, such as enhanced optical storage and real-time imaging through complex media. DL offers novel solutions for data interpretation challenges presented by experimental fluctuations and undersampling.

Conclusion and Future Prospects

The paper stresses that despite notable progress, DL models face inherent challenges due to their black-box nature, potential data biases, and the computational demands of high-quality training data. Nevertheless, the accelerating capabilities of DL in nano-photonics signal promising future developments. The paper posits that DL could be pivotal in advancing next-generation photonic systems, potentially leading to breakthroughs in quantum machine learning and real-time photonic system control.

In conclusion, while the integration of deep learning into nano-photonics is not without its obstacles, this intersection holds substantial promise for transformative advances in both the theoretical and practical realms of nano-photonic design and experimentation. Future work should focus on overcoming the limitations of DL models and expanding their applicability and interpretability in complex photonic systems.