Mr. Ronald P. Jenkins Profile

Ronald P. Jenkins

at Pennsylvania State Univ

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Publications (2)

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Proceedings Article | 3 October 2022 Presentation + Paper

A toolkit for multiscale optical system inverse-design

Sawyer Campbell, Ronald Jenkins, Benjamin Zerbe, Eric Whiting, Manushanker Balasubramanian, Arkaprovo Das, Jacob Young, Douglas Werner, Pingjuan Werner

Proceedings Volume 12217, 122170C (2022) https://doi.org/10.1117/12.2633581

KEYWORDS: Optical design, Systems modeling, Optical components, GRIN lenses, Chemical elements, Algorithm development, Optimization (mathematics), Nanofabrication, Human-machine interfaces, Gradient-index optics

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Optical system design is undergoing a transformational change with the increases in computational horsepower, cutting-edge algorithmic developments, and the advent of new nanofabrication technologies. Among the most exciting advances in recent memory are optical metasurfaces, which are patterned surfaces commonly realized through nanofabrication that can imbue optical engineers with expanded degrees of design freedom due to their ability to exploit the generalized form of Snell’s law. Through intelligent optimization and design, metasurfaces can be constructed which achieve arbitrary chromatic dispersion behaviors and unprecedented control over polarization that simply cannot be realized with conventional optical elements. However, designing high-performance metasurfaces requires the use of full-wave simulation tools and numerical optimization techniques which necessitate considerable computational resources. Moreover, while optical metasurfaces are moving towards millimeter and centimeter scale diameter lenses with advances in nanofabrication techniques, it is computationally infeasible to employ full-wave simulation tools directly to model optical systems that use such large size elements. Nevertheless, the size, weight, and power (SWaP) advantages afforded by optical metasurfaces make them a compelling choice for designers to consider in a number of applications, which are currently limited by bulky conventional optical solutions. Therefore, techniques that can rapidly model metasurfaces in conjunction with conventional optical elements such as lenses, mirrors, and prisms are highly desirable. In this presentation, we highlight a toolkit of custom solvers, design procedures, and powerful optimization algorithms that simplify and accelerate the development of hybrid optical systems with arbitrary combinations of conventional and metasurface elements.

Proceedings Article | 1 August 2021 Presentation + Paper

Discovering metasurface robustness through deep learning

Ronald Jenkins, Sawyer Campbell, Douglas Werner, Pingjuan Werner

Proceedings Volume 11795, 1179509 (2021) https://doi.org/10.1117/12.2593465

KEYWORDS: Tolerancing, Nanophotonics, Neural networks, Electron beam lithography, Diffraction, Optimization (mathematics), Nanofabrication, Electromagnetism, Electrical engineering, Data modeling

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Deep learning has recently become an important part of nanophotonic device design, with many researchers leveraging the power of neural networks to aid in inverse-design analysis. Acting as surrogate models for full-wave solvers, neural networks are now employed to develop new metasurface elements and gain insight into the underlying physics which dictate their behavior. A new avenue of discovery, which has been facilitated by the recent developments in deep learning, lies in the domain of metasurface robustness, a subject that presents many challenges to traditional solvers. Characterizing even relatively simple designs with a full-wave solver may be computationally expensive enough to make optimization challenging or even intractable. On the other hand, robustness must be measured by introducing some form of tolerance analysis into an optimization. Structures must be perturbed many times over, perhaps even in an exhaustive fashion. When the process is further complicated by considering complex design parameterizations (with many degrees of freedom), these full-wave optimizations are no longer tractable. However, deep neural networks can help to counter these challenges owing to their GPU speedup and powerful learning characteristics. By evaluating many full-wave metasurface designs ahead-oftime and investing them into neural network training, the network can then be used in tolerance analysis for substantial speedups down the road. This work showcases how we designed a suitable neural network for this purpose, as well as several studies conducted using this deep learning platform in this important area of metasurface robustness.

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