https://scholar.gist.ac.kr/handle/local/7919 2026-08-08T03:31:31Z https://scholar.gist.ac.kr/handle/local/25158 Title: 나노 단위의 압전전기장 조절을 통한 테라파 발진과 주파수 조절 Author(s): 정훈일; 정지훈; 이동선; C. J. Stanton; 조영달 2010-01-21T15:00:00Z https://scholar.gist.ac.kr/handle/local/15699 Title: ZnO/p-GaN Heterostructure for Solar Cells and the Effect of ZnGa2O4 Interlayer on Their Performance Author(s): Nam, Seung Yong; Choi, Yong Seok; Lee, Ju Ho; Park, Seong-Ju; Lee, Jeong Yong; Lee, Dong-Seon Abstract: We report the usage of ZnO material as an alternative for n-GaN for realizing III-nitride based solar cell. The fabricated solar cell shows large turn-on voltage of around 8 volts and a rapid decrease of photocurrent at low bias voltage under darkness and 1-sun illumination conditions, respectively. This phenomenon can be attributed to the formation of high-resistive ultra-thin layers at the ZnO/p-GaN junction interface during high temperature deposition. Transmission electron microscopy (TEM) studies carried out on the grown samples reveals that the ultra-thin layer consists of ZnGa2O4. It is found that the presence of insulating ZnGa2O4 film is detrimental in the performance of proposed heterostructure for solar cells. 2012-12-31T15:00:00Z https://scholar.gist.ac.kr/handle/local/32429 Title: Wing Shape Optimization of Underwater Floats with Motion Constraints Author(s): Lee, Donggun; Choi, Seongim S.; Chao, Yi; Park, Jong-chun Abstract: This study investigates the effects of attaching wing shapes to the body of an underwater float, comparing its motion with and without wings. The body is limited to vertical motion. However, with wings attached, it can achieve horizontal and rotational movements, such as sawtooth and spiraling motions. These wings are effective in controlling the motion of the float in response to water flow and reducing drag. To design the wing airfoil shapes for the underwater float, we utilized a Conditional Deep Convolutional Generative Adversarial Network(DCGAN) model to achieve inverse design with high lift-to-drag ratios and lift coefficients. This model extends the basic GAN structure by adding conditions to the inputs of the generator and discriminator, enabling the generation of airfoil shapes with desired aerodynamic performance. Prior to planform optimization, aerodynamic coefficients were compared using a baseline shape for five candidate wing attachment positions. A total of 77 shapes were generated by varying the span and chord parameters within a ±23% range of the baseline values. These shapes were used to train a Gaussian Process Regression (GPR) model, which served as a surrogate model during the optimization process. Dynamic analysis was conducted at an underwater float angle of 35.5° to determine the objective function and constraints. Finally, planform optimization was performed using a Genetic Algorithm (GA). The results demonstrate the potential of wings to enhance the motion control and drag reduction of underwater floats, offering significant improvements in hydrodynamic performance. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. 2025-01-09T15:00:00Z https://scholar.gist.ac.kr/handle/local/32211 Title: Wafer-scale fabrication of memristive passive crossbar circuits for brain-scale neuromorphic computing Author(s): Choi, Sanghyeon; Bezugam, Sai Sukruth; Bhattacharya, Tinish; Kwon, Dongseok; Strukov, Dmitri B. Abstract: Memristive passive crossbar circuits hold great promise for neuromorphic computing, offering high integration density combined with massively parallel operation. However, scaling up the integration complexity of such circuits remains challenging due to low device yield, stemming from the intrinsic properties of filamentary switching and limitations in current crossbar fabrication technologies. Here, we report a scalable passive crossbar device technology achieved through a co-design approach for memristors and crossbar structures. The proposed hardware platform is fabricated using CMOS-compatible processes without complex and high-temperature steps, enabling high device yield along with reliable and multibit operation. Importantly, the fabrication process is successfully scaled to a 4-inch wafer, maintaining an average device yield (>similar to 95%) and preserving key switching characteristics. The potential of this platform is showcased by implementing image classification of the fashion MNIST benchmark with an ex-situ trained spiking neural network. We believe that our work represents a significant step toward brain-scale neuromorphic computing systems. 2025-09-30T15:00:00Z