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Recent advances in chiral nanomaterials with unique electric and magnetic properties
Kwon Junyoung,Choi Won Jin,정의창,정욱진,황인국,Park Ki Hyun,Ko Seowoo Genevieve,Park Sung Min,Kotov Nicholas A.,염지현 나노기술연구협의회 2022 Nano Convergence Vol.9 No.32
Research on chiral nanomaterials (NMs) has grown radically with a rapid increase in the number of publications over the past decade. It has attracted a large number of scientists in various fields predominantly because of the emergence of unprecedented electric, optical, and magnetic properties when chirality arises in NMs. For applications, it is particularly informative and fascinating to investigate how chiral NMs interact with electromagnetic waves and magnetic fields, depending on their intrinsic composition properties, atomic distortions, and assembled structures. This review provides an overview of recent advances in chiral NMs, such as semiconducting, metallic, and magnetic nanostructures.
Thickness-dependent Schottky barrier height of MoS2field-effect transistors
Kwon, Junyoung,Lee, Jong-Young,Yu, Young-Jun,Lee, Chul-Ho,Cui, Xu,Hone, James,Lee, Gwan-Hyoung The Royal Society of Chemistry 2017 Nanoscale Vol.9 No.18
<P>2D semiconductors, including transition metal dichalcogenides (TMDs), have been widely studied recently. However, the device performance is deteriorated due to the significant contact resistance. The contact resistance of MoS2-metal contacts decreases with the thickness of MoS2. We obtained a Schottky barrier height as low as about 70 meV when MoS2 is trilayer-thick. It is important to find the optimal choice of contact metal and layer thickness of MoS2.</P>
Junyoung Park,Joonsoo Kwon,Jinwook Oh,Seungjin Lee,Joo-Young Kim,Hoi-Jun Yoo IEEE 2012 IEEE journal of solid-state circuits Vol.47 No.11
<P>A low-power real-time traffic sign recognition system that is robust under various illumination conditions is proposed. It is composed of a Retinex preprocessor and an SVM processor. The Retinex preprocessor performs the Multi-Scale Retinex (MSR) algorithm for robust light and dark adaptation under harsh illumination environments. In the Retinex preprocessor, the recursive Gaussian engine (RGE) and reflectance engine (RE) exploit parallelism of the MSR tasks with a two-stage pipeline, and a mixed-mode scale generator (SG) with adaptive neuro-fuzzy inference system (ANFIS) performs parameter optimizations for various scene conditions. The SVM processor performs the SVM algorithm for robust traffic sign classification. The proposed algorithm-optimized small-sized kernel cache and memory controller reduce power consumption and memory redundancy by 78% and 35%, respectively. The proposed system is implemented as two separated ICs in a 0.13-μm CMOS process, and the two chips are connected using network-on-chip off-chip gateway. The system achieves robust sign recognition operation with 90% sign recognition accuracy under harsh illumination conditions while consuming just 92 mW at 1.2 V.</P>