Hanyang University · 工学
Professor Hsiao L. Chung's research lab specializes in the inverse design of nanophotonic and metasurface devices, leveraging advanced computational methods such as adjoint-based optimization and machine learning to achieve unprecedented control over light manipulation. The lab focuses on creating high-efficiency, broadband, and tunable optical components—such as metalenses and metasurfaces—capable of operating across diverse numerical apertures and dynamic conditions. Key research directions include free-form nanophotonic design, computational electromagnetics, and data-efficient machine learning for photonic device discovery.
Figures are computed from collected data and may differ slightly.
We use inverse design to discover metalens structures that exhibit broadband, achromatic focusing across low, moderate, and high numerical apertures. We show that standard unit-cell approaches cannot achieve high-efficiency high-NA focusing, even at a single frequency, due to the incompleteness of the unit-cell basis, and we provide computational upper bounds on their maximum efficiencies. At low NA, our devices exhibit the highest theoretical efficiencies to date. At high NA-of 0.9 with transla
Tunable metasurfaces have demonstrated the potential for dramatically enhanced functionality for applications including sensing, ranging and imaging. Liquid crystals (LCs) have fast switching speeds, low cost, and mature technological development, offering a versatile platform for electrical tunability. However, to date, electrically tunable metasurfaces are typically designed at a single operational state using physical intuition, without controlling alternate states and thus leading to limited
Nanophotonic devices have enabled microscopic control of light with an unprecedented spatial resolution by employing subwavelength optical elements that can strongly interact with incident waves. However, to date, most nanophotonic devices have been designed based on fixed-shape optical elements, and a large portion of their design potential has remained unexplored. It is only recently that free-form design schemes have been spotlighted in nanophotonics, offering routes to make a break from conv
Recent advancements in inverse design approaches, exemplified by their large-scale optimization of all geometrical degrees of freedom, have provided a significant paradigm shift in photonic design. However, these innovative strategies still require full-wave Maxwell solutions to compute the gradients concerning the desired figure of merit, imposing, prohibitive computational demands on conventional computing platforms. This review analyzes the computational challenges associated with the design
Amorphous silicon/crystalline silicon (a-Si/c-Si) micromorph tandem cells, with best confirmed efficiency of 12.3%, have yet to fully approach their theoretical performance limits. In this work, we consider a strategy for improving the light trapping and charge collection of a-Si/c-Si micromorph tandem cells using random texturing with adjustable short-range correlations and long-range periodicity. In order to consider the full-spectrum absorption of a-Si and c-Si, a novel dispersion model known
Innovative machine learning techniques have facilitated the inverse design of photonic structures for numerous practical applications. Nevertheless, the quantity of data and the initial data distribution are paramount for the discovery of highly efficient photonic devices. These devices often require simulated data ranging from thousands to several hundred thousand data points. This issue has consistently posed a major hurdle in machine learning-based photonic design problems. Therefore, we prop
A key challenge in photovoltaics today is to develop cell technologies with both higher efficiencies and lower fabrication costs than incumbent crystalline silicon (c-Si) single-junction cells. While tandem cells have higher efficiencies than c-Si alone, it is generally challenging to find a low-cost, high-performance material to pair with c-Si. However, the recent emergence of 22% efficient perovskite photovoltaics has created a tremendous opportunity for high-performance, low-cost perovskite /
We demonstrate an axisymmetric inverse-designed metalens to improve the performance of zone-plate-array lithography (ZPAL), one of the maskless lithography approaches, that offer a new paradigm for nanoscale research and industry. First, we derive a computational upper bound for a unit-cell-based axisymmetric metalens. Then, we demonstrate a fabrication-compatible inverse-designed metalens with 85.50% transmission normalized focusing efficiency at 0.6 numerical aperture at 405 nm wavelength; a h
Over the past decade, significant advancements in high-resolution imaging technology have been driven by the miniaturization of pixels within image sensors. However, this reduction in pixel size to submicrometer dimensions has led to decreased efficiency in color filters and microlens arrays. The development of color routers that operate at visible wavelengths presents a promising avenue for further miniaturization. Despite this, existing color routers often encounter severe interpixel crosstalk
Optical vortices (OVs) have rapidly varying spatial phase and optical energy that circulates around points or lines of zero optical intensity. Manipulation of OVs offers innovative approaches for various fields, such as optical sensing, communication, and imaging. In this work, we demonstrate the correlation between OVs and absorption enhancement in two types of structures. First, we introduce a simple planar one-dimensional (1D) structure that manipulates OVs using two coherent light sources. T
Integrated silicon photonic waveguide biosensors have shown great potential for detecting bio-molecules because they enable efficient device functionalization via a well-developed surface chemistry, as well as simple scalable manufacturing, which makes them particularly suitable for low-cost point-of-care diagnostic. The on-chip integrated biosensors can be broadly classified into two types: (i) high-quality factor resonator sensors and (ii) interferometric sensors relying on non-resonant optica
Thermophotovoltaics (TPV) convert heat into electricity by capturing thermal radiation with a photovoltaic (PV) cell, ideally at efficiencies of 50% or more. However, excess heating of the PV cell from close proximity to the emitter substantially reduces the system efficiency. In this work, we theoretically develop and numerically demonstrate an approach to fundamentally improving TPV systems that allow for a much greater separation of an emitter and a receiver. Thus, we solve the excess heating
Tandem solar cells consisting of high bandgap cadmium telluride alloys atop crystalline silicon have potential for high efficiencies exceeding the Shockley-Queisser limit. However, experimental results have fallen well below this goal significantly because of non-ideal current matching and light trapping. In this work, we simulate cadmium zinc telluride (CZT) and crystalline silicon (c-Si) tandems as an exemplary system to show the role that a hybrid light trapping and bandgap engineering approa
Thin-film lithium niobate (TFLN) has emerged as a promising platform for integrated photonics due to its exceptional material properties. The application of freeform topology optimization to TFLN devices enables the realization of compact designs with complex functionalities and high efficiency. However, the stringent fabrication constraints of TFLN present significant challenges for optimization, particularly in nonlinear photonic devices. In this work, we propose an inverse design methodology
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