A study published in ACS Photonics explains how deep learning and conventional optimization-based methods create a new conceptual basis for free-form optical engineering. These algorithm-based methodologies will help scientists and engineers streamline optical systems near the physical boundaries of structured media.
Importance of Freeform Design in Optical Systems
Understanding and utilizing the relationship between electromagnetic response and geometric shape optimization enables the experimental implementation of optical systems in the discipline of optical engineering.
This relationship was based primarily on physical laws. However, a combination of simplifications, approximations and limitations of physical systems has led to intuitive models and design techniques.
Recent advances in high-precision manufacturing have opened up new possibilities for the experimental patterning of geometric features, which in turn has the potential to reshape the optical engineering process.
Modern machining techniques allow the fabrication of refractive optical components with arbitrary profiles. Additive manufacturing generates nearly arbitrary 3D structures with feature sizes ranging from macro to submicrometer scale. In addition, lithography allows the routine structuring of free-form thin films at the nanometer scale.
Each voxel in the pattern process is a free design parameter, which makes the geometric feature space for optical design presented by these manufacturing processes meaningful. This transforms the design and manufacturing workflow of photonic technologies.
How will deep learning algorithms streamline the design of optical systems?
Algorithmic optical engineering tools redefine the conceptual underpinnings of the approach to optical system design.
By approaching the relationship between optical response and device as a physics-constrained data relationship, algorithm-based methods can discover new, non-intuitive structure-function solutions, as opposed to standard designs based on physical models. intuitive established by people.
Data science algorithms play a vital role in this progress. However, they have proven particularly destructive in photonics, due to the well-developed quantitative physical foundations of the field.
Full-wave Maxwell simulators effectively act as ground truth oracles, and they can be used to calculate structure-function solutions to quickly generate training data or meticulously calculate gradients quickly.
Data science algorithms make explicit use of physics and are particularly good at learning and taking advantage of non-intuitive structure-function connections.
Recent years have seen a confluence of factors that have enabled the emergence of a new generation of optical engineering. These factors include the rapid development of machine learning, improvements in optimization theory, the production of new computing hardware, and the continuous improvement of experimental manufacturing tools.
For these tools to be widely recognized and used by the photonics community, many developments will be necessary.
Processes must be scalable for large domains of any size without sacrificing accuracy. This is a problem for many algorithms, especially data science algorithms with fixed and usually narrow domain dimensions.
The interface between electromagnetism and data science and the interaction between neural network architecture and scientific computing operations need to be further improved.
The algorithms designed must be open to the public, robust and simple to use, and many of these techniques require the user to have extremely specialized knowledge in data science, free-form optimization or physics.
The widespread use of machine learning principles and their adaptation to user-specific situations will require the development of algorithms that use automated and efficient hyperparameter tuning, neural network design specification, and active learning techniques. .
The study of electromagnetic performance limit calculations is still in its infancy and non-trivial limits have only recently been established for a small number of photonic devices.
While many of these ideas provide limited device design and execution solutions, comprehensible algorithmic approaches will enable more fundamental advances in optical engineering that connect unintuitive structure-function solutions to broader physical insights.
The algorithms will enable new paradigms in optical engineering. One example is the creation of true multiscalar optical design and production platforms that include diffractive, refractive, and full-wave scalar optics.
Optical systems at each length scale have unique characteristics. However, these modalities can be synergistic, resulting in systems with excellent bandwidth, functional multiplexing, and aberration correction qualities. They also open up new possibilities for connecting on-chip and free-space optical systems.
There are huge prospects for connecting optical engineering to non-electromagnetic physical and software domains, resulting in optical systems with entirely new functionality.
Mingkun Chen, Jiaqi Jiang, and Jonathan A. Fan. (2022). Algorithm-driven paradigms for free-form optical engineering. ACS Photonics. https://pubs.acs.org/doi/10.1021/acsphotonics.2c00612