https://scholarworks.unist.ac.kr/handle/201301/37 2026-05-13T04:14:38Z https://scholarworks.unist.ac.kr/handle/201301/91671 Title: Dual-Band Dual-Circularly Polarized Holographic Metasurface Antenna With Mode-Selective Electromagnetic Band Gap Author(s): Nguyen, Thi Duyen; Byun, Gangil Abstract: This article presents a dual-band holographic metasurface antenna (HMsA) capable of generating steered beams with both right-hand (RH) and left-hand (LH) circular polarization (CP). The proposed unit cell consists of three stacked layers above a ground plane. The first layer features a mode-selective electromagnetic bandgap structure that effectively isolates transverse magnetic (TM) and transverse electric (TE) surface-wave modes. Such isolation allows independent operation at distinct frequencies for various beam directions, while maintaining radiation efficiency comparable to single-mode designs. The second layer employs split-ring elements designed for the TE mode, and the third layer utilizes split-patch patterns for the TM mode. The dual-port configuration provides distinct polarization control: the TM mode at 11 GHz, excited by a vertical monopole, achieves RHCP, while the TE mode at 14 GHz, excited by a horizontal dipole, produces LHCP. Each port supports directional beam radiation at 30 degrees and 20 degrees, respectively, with measured gains of 17.47 and 18.01 dBi, and corresponding axial ratios of 0.53 and 0.43 dB. 2026-03-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/91665 Title: Advantages of Broadband Metalenses for Generalizable Image Classification Author(s): Zhang, Yubo; Froch, Johannes; Xiang, Jinlin; Colburn, Shane; Lee, Myunghoo; Zhou, Zhihao; Choi, Minho; Shlizerman, Eli; Majumdar, Arka Abstract: Optical neural networks (ONNs) are gaining increasing attention to accelerate machine learning tasks. In particular, static meta-optical encoders designed for task-specific preprocessing have demonstrated orders of magnitude smaller energy consumption over purely digital counterparts, albeit at the cost of a slight degradation in classification accuracy. However, a lack of generalizability poses serious challenges for wide deployment of static meta-optical front-ends. Here, we investigate the utility of a single-layer metalens as a meta-optical encoder in ONNs for generalizable image classification. Specifically, we show that a visible-spectrum broadband metalens can achieve image classification accuracy comparable to high-end, sensor-limited optics and consistently outperforms the corresponding hyperboloid baseline across a wide range of sensor pixel sizes and digital backends. We further design an end-to-end optimized single-aperture metasurface for ImageNet classification and observe that the optimization tends to balance the modulation transfer function (MTF) across wavelengths within the sensor-detectable passband. Together, these observations suggest that the preservation of spatial-frequency information is an important factor influencing the performance of ONNs. Our results provide physical insight into the process of task-driven optical optimization and offer practical guidance for the design of high-performance ONNs and meta-optical encoders for generalizable computer-vision tasks. 2026-03-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/91645 Title: A Framework of Automated LC-VCO Design with Physical Layout Based on Reinforcement Learning Author(s): Kim, Sungjin; Lee, Hyunsoo; Hong, Seongmin; Yoon, Heein; Song, Taigon Abstract: Artificial Intelligence (AI) has been increasingly utilized across various fields, including communications, healthcare, and Computer-Aided Design (CAD). However, AI has shown relatively limited advances in analog and RF circuit design, which are critical for modern communication systems such as 5G due to their higher complexity and nonlinear characteristics. For example, the Inductor-Capacitor Voltage-Controlled Oscillator (LC-VCO) is a crucial component in frequency synthesizers, determining the performance of RF systems, including high data transmission rates and wide bandwidth. In fact, LC-VCO design is challenging due to the high parameter variability and complex interactions between design variables, making it challenging to optimize parameters to meet target specifications. Thus, this study proposes a comprehensive LC-VCO design methodology compatible across multiple process nodes and supports optimization down to the layout level. We use Reinforcement Learning (RL) to navigate the nonlinear design space efficiently for schematic optimization and apply an algorithm from Gradient Descent to optimize the design at the physical layout level. We highlight that the versatility of our methodology is demonstrated by producing optimized Figures of Merit (FoM) across various technology nodes and frequency ranges, showcasing its potential as a universal design tool accessible to all users. 2026-02-28T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/91638 Title: Measurement-based infinitesimal dipole modeling for long-range OAM mode antenna analysis at E-band Author(s): Moon, Seok Ju; Jeong, Jaehoon; Kim, Young Dam; Choi, EunMi Abstract: Orbital angular momentum (OAM) beams exhibit helical wavefronts characterized by topological charges, enabling multiple orthogonal modes to coexist on the same frequency channel. While this property offers potential gains in channel capacity, practical characterization of OAM beams remains challenging due to their strong divergence and the large measurement ranges required for long-distance analysis. This paper proposes a measurement-based prediction framework using infinitesimal dipole modeling (IDM) to estimate long-range OAM beam characteristics from near-field data. The electric field at the OAM aperture is represented by an equivalent set of infinitesimal dipoles obtained through an inverse dyadic Green's function formulation, enabling far-field reconstruction without additional long-range measurements. The method is experimentally validated using spiral phase plate (SPP) antennas operating in the E-band, and the predicted OAM intensity, phase, and mode-composition profiles show high agreement with measurements, yielding a correlation coefficient (CCF) above 0.9, validating the model's fidelity. The results demonstrate that IDM provides a compact and scalable framework for analyzing OAM propagation over extended distances, offering practical benefits for OAM-based wireless communication, sensing, and antenna evaluation. 2026-03-31T15:00:00Z