Current Optics and Photonics
Vol. 8 No. 6 (Dec. 2024)
한국광학회지
Vol. 35 No. 6(Dec. 2024)
K-Light
vol. 7, no. 4(Aug. 2024)
한국광학회 공식 SNS 계정 바로가기
트위터
인스타그램
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[Editor's Pick] Current Optics and Photonics Vol. 8 no. 6 (2024 December)_2nd
Binary-optimization-based Multilayers and Their Practical Applications
Geon-Tae Park, Rira Kang, Byunghong Lee, and Sun-Kyung Kim*
Current Optics and Photonics Vol. 8 No. 6 (2024 December), pp. 545-561
DOI: https://doi.org/10.3807/COPP.2024.8.6.545
Fig. 1 Workflow of binary-optimization-based multilayer design and its applications. (a) Schematic of the iterative optimization cycle, composed of four primary steps [53]. (b) (i) Schematic of antireflective coatings (ARC) applied to a lens, designed to minimize reflectance at the target wavelength λ0 over a wide range of incident angles θ. (ii) Illustration of transparent radiative coolers (TRCs) for energy-saving windows, engineered to reflect ultraviolet and near-infrared light while transmitting visible light, and maintaining high emissivity within the atmospheric window. The red line represents the target transmittance spectrum, and the blue line represents the target emissivity spectrum for an ideal transparent radiative cooler. (iii) Schematic of bandpass filters for thermophotovoltaics (TPVs), designed to enhance the efficiency of the photovoltaic (PV) cell by selective emission. The red dashed line represents the spectral irradiance of a blackbody IBB, and the blue solid line represents the external quantum efficiency (EQE) multiplied by the intensity of blackbody radiation (IBB). The green solid line shows the target spectrum of a selective emitter with unit emissivity.
Keywords: Binary optimization, Machine learning, Multilayer, Optical coating, Optical design
OCIS codes: (200.0200) Optics in computing; (220.0220) Optical design and fabrication; (310.0310) Thin films; (310.1210) Antireflection coatings; (310.6845) Thin film devices and applications
Abstract
Multilayers composed of two or more materials enable the regulation of transmission, reflection, and absorption spectra across one or multiple bands. While analytic formulas based on well-established interference conditions, such as those employed in single-, double-, and triple-layer antireflective coatings and distributed Bragg reflectors, have provided suitable solutions for traditional optical coatings, they are limited in achieving the intricate spectral characteristics required by multifunctional optical coatings. To overcome this limitation, a variety of machine learning-based design algorithms have been rigorously studied. Among these, binary optimization has proven particularly effective for designing multilayer optical coatings. This approach transforms a given multilayer into a binary vector with multiple bits, where each bit represents one of the constituent materials, and quickly identifies an optimal figureof-merit by analyzing the interactions among the elements of the binary vector. In this review article, we elucidate the principles of binary optimization and explore its applications in the design of multilayers for antireflective coatings for high-numerical-aperture lenses, transparent radiative coolers for energysaving windows, and bandpass filters for thermophotovoltaics. Furthermore, we address the limitations, challenges, and perspectives of machine learning-based optical design to guide directions for future research in this field.
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[Editor's Pick] Current Optics and Photonics Vol. 8 no. 6 (2024 December)_1st
A Tutorial on Inverse Design Methods for Metasurfaces
Jin-Young Jeong† , Sabiha Latif† , and Sunae So*
Current Optics and Photonics Vol. 8 No. 6 (2024 December), pp. 531-544
DOI: https://doi.org/10.3807/COPP.2024.8.6.531
Fig. 1 Schematic illustration of an overview of inverse design metasurfaces using machine learning and optimization methods.
Keywords: Inverse design, Machine learning, Metasurface, Optimization algorithm
OCIS codes: (150.1135) Algorithms; (240.0240) Optics at surfaces
Abstract
This paper provides a tutorial on inverse design approaches for metasurfaces with a systematic analysis of the fundamental methodologies and underlying principles for achieving targeted optical properties. Traditionally, metasurfaces have been designed with extensive trial-and-error methods using analytical modeling and numerical simulations. However, as metasurface complexity grows, these conventional techniques become increasingly inefficient in exploring the vast design space. Recently, machine learning and optimization algorithms have emerged as powerful tools for overcoming these challenges and enabling more efficient and accurate inverse design. We begin by introducing the fundamentals of optical simulations used for forward modeling of metasurfaces and their relevance to inverse design. Next, we explore recent advancements in applying machine learning techniques such as neural networks, Markov decision processes, and Monte Carlo simulations, as well as optimization algorithms, including automatic differentiation, the adjoint method, genetic algorithms, and particle swarm optimizations, and show their potential to revolutionize the metasurface design process. Finally, we conclude with a summary of key findings and insights from this review.
