75 research outputs found

    Political identity and financial risk taking: Insights from social dominance orientation

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    This article investigates how peopleโ€™s political identity is associated with their financial risk taking. The authors argue that conservativesโ€™ financial risk taking increases as their self-efficacy increases because of their greater social dominance orientation, whereas liberalsโ€™ financial risk taking is invariant to their self-efficacy. This central hypothesis is verified in six studies using different measures of political identity, self-efficacy, and financial risk taking. The studies also use different samples of U.S. consumers, including online panels, a large-scale data set spanning five election cycles, and a secondary data set of political donations made by managers at companies. Finally, the authors articulate and demonstrate the mediating effect of individualsโ€™ focus on the upside potential of a decision among conservatives but not liberals

    Quantitative agreement of Dzyaloshinskii-Moriya interactions for domain-wall motion and spin-wave propagation

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    The magnetic exchange interaction is the one of the key factors governing the basic characteristics of magnetic systems. Unlike the symmetric nature of the Heisenberg exchange interaction, the interfacial Dzyaloshinskii-Moriya interaction (DMI) generates an antisymmetric exchange interaction which offers challenging opportunities in spintronics with intriguing antisymmetric phenomena. The role of the DMI, however, is still being debated, largely because distinct strengths of DMI have been measured for different magnetic objects, particularly chiral magnetic domain walls (DWs) and non-reciprocal spin waves (SWs). In this paper, we show that, after careful data analysis, both the DWs and SWs experience the same strength of DMI. This was confirmed by spin-torque efficiency measurement for the DWs, and Brillouin light scattering measurement for the SWs. This observation, therefore, indicates the unique role of the DMI on the magnetic DW and SW dynamics and also guarantees the compatibility of several DMI-measurement schemes recently proposed.Comment: 24 pages, 5 figure

    ์ค‘๊ธˆ์†/๊ฐ•์ž์„ฑ ๋‹ค์ธต๋ฐ•๋ง‰์—์„œ ๋น„๋Œ€์นญ ๊ตํ™˜์ƒํ˜ธ์ž‘์šฉ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ์Šคํ•€ํŒŒ (spin wave), ์Ÿ๋กœ์‹ ์Šคํ‚ค-๋ชจ๋ฆฌ์•ผ ์ƒํ˜ธ์ž‘์šฉ (Dzyaloshinskii-Moriya interaction), ์ˆ˜์ง ์ž๊ธฐ์ด๋ฐฉ์„ฑ (perpendicular magnetic anisotropy), ๋ธŒ๋ฆด๋ฃจ์•™ ๊ด‘ ์‚ฐ๋ž€ ๋ถ„๊ด‘๊ณ„ (Bril-louin light scattering spectroscopy), ์ž๊ธฐ๊ด‘ํ•™ ์ปค ํšจ๊ณผ (magneto-optical Kerr effect)Recently, the interfacial Dzyaloshinskii-Moriya interaction has been intensively investigated in the spotlight. This phenomenon is significantly considered due to potential application as it could open new paths to manipulate a quasiparticle information based on skyrmion. From several reports, the magnetic skyrmion state is well-known depending on Dzyaloshinskii-Moriya interaction energy density. To investigate this fun-damental asymmetric exchange interaction, the exact and reliable method in the quantitative determination of the interfacial Dzyaloshinskii-Moriya interaction energy density is highly required. Therefore, we study the magnetic thin film structure of the heavy metal (HM)/ferromagnet (FM) multilayers involving the perpendic-ular magnetic anisotropy obtained from the strong spin-orbit coupling of the heavy metal layer in this Thesis. Especially, we focus on the interfacial Dzyaloshinskii-Moriya interaction energy density and the perpendicu-lar magnetic anisotropy. For the measurements, the Brillouin light scattering spectroscopy, hence, is mainly used. This measure-ment system is a well-known multi-purpose tool to investigate the thermally excited propagating spin wave to obtain the fundamental magnetic properties such as the perpendicular magnetic anisotropy, the saturation magnetization, the exchange stiffness constant, and the magnetic damping simultaneously. Furthermore, it can be directly determined the Dzyaloshinskii-Moriya interaction derived from the spin wave frequency dif-ferences of propagating spin waves in the spectrum of Brillouin light scattering spectroscopy. The main results are composed in the two parts such as the HM1/ferromagnet/ HM2 heterostructures depending on the stack-ing order of the HM, and the investigating method with anti-reflection layer for an accurate determination of interfacial Dzyalshinskii-Moriya interaction energy density in the HM/FM multilayers. These results are shown the potentiality for the finding of a stable skyrmion state and the more precise interfacial Dzyaloshinskii-Moriya interaction energy density by an using anti-reflection layer.| ์Ÿ๋กœ์‹ ์Šคํ‚ค-๋ชจ๋ฆฌ์•ผ (Dzyaloshinskii-Moriya, DM)์ƒํ˜ธ์ž‘์šฉ์€ ๊ตํ™˜ ์ƒํ˜ธ์ž‘์šฉ์˜ ํ•œ ์ข…๋ฅ˜๋กœ, ํ•˜์ด์  ๋ฒ ๋ฅดํฌ ๊ตํ™˜ ์ƒํ˜ธ์ž‘์šฉ๊ณผ ๋‹ค๋ฅด๊ฒŒ ์Šคํ•€๊ถค๋„ ๊ฒฐํ•ฉ์ด ํฐ ๋ฌผ์งˆ๊ณผ ์ธ์ ‘ํ•œ ์ž๊ธฐ๋ชจ๋ฉ˜ํŠธ ์‚ฌ์ด์—์„œ ์ž‘์šฉํ•˜๋Š” ๋น„๋Œ€์นญ ๊ตํ™˜ ์ƒํ˜ธ์ž‘์šฉ์ด๋‹ค. ์ตœ๊ทผ DM ์ƒํ˜ธ์ž‘์šฉ์€ ์ „๋ฅ˜์— ์˜ํ•ด ๋™์ž‘ํ•˜๋Š” ์ž๊ตฌ ๋ฒฝ์˜ ์ด๋™์†๋„๋ฅผ ํ˜„์ €ํžˆ ์ฆ๊ฐ€์‹œํ‚ค๋ฉฐ, ํŠนํžˆ ์Šคํ•€๋“ค์ด ์†Œ์šฉ๋Œ์ด ๋ชจ์–‘์œผ๋กœ ๋ฐฐ์—ด๋˜๋Š” ์ž๊ธฐ ์Šค์ปค๋ฏธ์˜จ ๊ตฌ์กฐ๋ฅผ ํ˜•์„ฑํ•˜๋Š”๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค๋Š” ๊ฒƒ์ด ๋ณด๊ณ ๋˜์—ˆ๋‹ค. ์ž๊ธฐ ์Šค์ปค๋ฏธ์˜จ์€ ์ €์ „๋ ฅ, ๊ณ ๋ฐ€๋„, ์ดˆ๊ณ ์†์œผ๋กœ ๊ตฌ๋™ ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์˜ˆ์ธก๊ณผ ์ค€์ž…์ž ์ƒํƒœ๋ฅผ ๊ฐ–๋Š”๋‹ค๋Š” ์žฅ์ ์œผ๋กœ ์ฐจ์„ธ๋Œ€ ์ž๊ธฐ๋ฉ”๋ชจ๋ฆฌ๋กœ ๊ฐ๊ด‘๋ฐ›๊ณ  ์žˆ๋Š” ์Šคํ•€๊ถค๋„ ๋Œ๋ฆผํž˜ ๊ธฐ๋ฐ˜ ์ž๊ธฐ๋ฉ”๋ชจ๋ฆฌ ์†Œ์ž๋ฅผ ๋น„๋กฏํ•˜์—ฌ ๋ฏธ๋ž˜์˜ ์Šคํ•€ํŠธ๋กœ๋‹‰์Šค ์†Œ์ž์˜ ์ค‘์š”ํ•œ ํ›„๋ณด๋กœ ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ ์•ˆ์ •์ ์ธ ์ž๊ธฐ ์Šค์ปค๋ฏธ์˜จ์„ ํ˜•์„ฑํ•˜๊ธฐ ์œ„ํ•ด DM ์ƒํ˜ธ์ž‘์šฉ๊ณผ ์ˆ˜์ง ์ž๊ธฐ์ด๋ฐฉ์„ฑ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ์ด๋ก ์ ์œผ๋กœ ์•ˆ์ •์ ์ธ ์ž๊ธฐ ์Šค์ปค๋ฏธ์˜จ ํ˜•์„ฑ์€ DM ์ƒํ˜ธ์ž‘์šฉ์„ ๊ฐ–๋Š” ์ž๊ตฌ ๋ฒฝ ์—๋„ˆ์ง€ ๋ฐ€๋„๊ฐ€ 0 ํ˜น์€ ์Œ์ˆ˜๊ฐ€ ๋˜๋Š” ๊ฒƒ์„ ์„ ํ˜ธํ•œ๋‹ค. ํ˜„์žฌ๊นŒ์ง€ ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ DM ์ƒํ˜ธ์ž‘์šฉ์„ ์ธก์ •ํ•˜๋Š” ๊ธฐ์ˆ ์ด ๊ฐœ๋ฐœ๋˜์—ˆ์œผ๋ฉฐ, ๊ด€๋ จ ์—ฐ๊ตฌ๋„ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ค‘๊ธˆ์†/๊ฐ•์ž์„ฑ ๋‹ค์ธต ์ž๊ธฐ๋ฐ•๋ง‰์˜ ๊ตฌ์กฐ์—์„œ ์ค‘๊ธˆ์†์ธต์˜ ๊ฐ•๋ ฅํ•œ ์Šคํ•€๊ถค๋„ ๊ฒฐํ•ฉ์œผ๋กœ ์–ป์€ DM ์ƒํ˜ธ์ž‘์šฉ๊ณผ ์ˆ˜์ง ์ž๊ธฐ์ด๋ฐฉ์„ฑ์˜ ๋ฌผ๋ฆฌ์  ํ˜„์ƒ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ๋ฌผ๋ฆฌ์  ํ˜„์ƒ์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ๋ธŒ๋ฆด๋ฃจ์•™ ๊ด‘ ์‚ฐ๋ž€ (Brillouin light scattering) ๋ฐฉ๋ฒ•์ด ์ฃผ์š”ํ•˜๊ฒŒ ์ด์šฉ๋˜์—ˆ๋‹ค. ์ด ์ธก์ •๋ฐฉ๋ฒ•์€ ์ž…์‚ฌํ•˜๋Š” ๋น›์„ ์ด์šฉํ•˜์—ฌ ์—ด์ ์œผ๋กœ ๋“ค๋œฌ ์Šคํ•€ํŒŒ๋ฅผ ์กฐ์‚ฌํ•˜์—ฌ DM ์ƒํ˜ธ์ž‘์šฉ, ์ˆ˜์ง ์ž๊ธฐ์ด๋ฐฉ์„ฑ, ํฌํ™” ์žํ™”, ๊ตํ™˜ ๋ปฃ๋ปฃํ•จ ์ƒ์ˆ˜, ์ž๊ธฐ ๊ฐ์‡  ์ƒ์ˆ˜ ๋“ฑ ๊ธฐ๋ณธ์ ์ธ ์ž๊ธฐ์  ํŠน์„ฑ์„ ๋™์‹œ์— ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค๋ชฉ์  ์ธก์ •๋„๊ตฌ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€์˜ ์ฃผ์š”ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์Šคํ•€๊ถค๋„ ์ƒํ˜ธ์ž‘์šฉ์˜ ๋ฌผ๋ฆฌ์  ํ˜„์ƒ์„ ๊ด€์ธกํ•˜๊ธฐ ์œ„ํ•ด ๋งˆ๊ทธ๋„คํŠธ๋ก  ์Šคํผํ„ฐ๋ง ์‹œ์Šคํ…œ์„ ์ด์šฉํ•˜์—ฌ ๊ตฌ์กฐ์ ์œผ๋กœ ๋™์ผํ•˜์ง€๋งŒ, ๋ฌผ์งˆ์˜ ์ ์ธต ์ˆœ์„œ๊ฐ€ ์—ญ์ „๋œ Pd/Co/Pt๊ณผ Pt/Co/Pd์˜ ์‹œ๋ฃŒ๋ฅผ ์ œ์ž‘ํ•˜์—ฌ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์‹คํ—˜๊ฒฐ๊ณผ ์ฆ์ฐฉ ์ˆœ์„œ์— ๋”ฐ๋ผ์„œ ์ˆ˜์ง ์ž๊ธฐ์ด๋ฐฉ์„ฑ์ด ํฌ๊ฒŒ ๊ฐ์†Œํ•จ์„ ์‹คํ—˜์ ์œผ๋กœ ๊ด€์ธกํ•˜์˜€๋‹ค. ์ด ํ˜„์ƒ์€ ์Šคํผํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฌผ์งˆ์˜ ์ฆ์ฐฉ ์ˆœ์„œ์— ๋”ฐ๋ผ์„œ ์นจํˆฌํ•˜๋Š” ์ •๋„๊ฐ€ ๋‹ค๋ฅด๊ณ , ์ด์— ๋”ฐ๋ผ์„œ ํ˜ผํ•ฉ์ด ์ผ์–ด๋‚˜๋Š” ์ •๋„๊ฐ€ ๋‹ฌ๋ผ์ง€๋Š” ๊ฒƒ์œผ๋กœ ํ•ด์„ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ˆ˜์ง ์ž๊ธฐ์ด๋ฐฉ์„ฑ์˜ ๊ทน์ ์ธ ๋ณ€ํ™”์—๋„ DM ์ƒํ˜ธ์ž‘์šฉ ์—๋„ˆ์ง€ ๋ฐ€๋„๋Š” ํฌ๊ฒŒ ๋ณ€ํ™”ํ•˜์ง€ ์•Š๋Š” ๋‹ค๋Š” ๊ฒƒ์„ ์‹คํ—˜์ ์œผ๋กœ ๊ด€์ธกํ•˜์˜€๋‹ค. ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์™ธ๋ถ€์ž๊ธฐ์žฅ์ด ์—†๋Š” ํ™˜๊ฒฝ์—์„œ Pd/Co/Pt ๊ตฌ์กฐ๋Š” ํŠน์ •ํ•œ Co ๋‘๊ป˜์—์„œ ์ž๊ตฌ ๋ฒฝ ์—๋„ˆ์ง€ ๋ฐ€๋„๊ฐ€ ์Œ์ˆ˜๊ฐ€ ๋˜๋Š” ์˜์—ญ์„ ๊ฐ–๋Š”๋‹ค๋Š” ๊ฒƒ์„ ๊ณ„์‚ฐ์„ ํ†ตํ•˜์—ฌ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” DM ์ƒํ˜ธ์ž‘์šฉ ์—๋„ˆ์ง€ ๋ฐ€๋„ ๊ฒฐ์ •๊ณผ ๊ด€๋ จ์ด ์žˆ๋‹ค. DM ์ƒํ˜ธ์ž‘์šฉ์„ ๊ด€์ธกํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋ธŒ๋ฆด๋ฃจ์•™ ๊ด‘ ์‚ฐ๋ž€ ๋ถ„๊ด‘๊ณ„๋Š” ํ•„์ˆ˜์ ์œผ๋กœ ์š”๊ตฌ๋˜๋Š” ์žฅ๋น„์ด๋‹ค. ๊ทผ๋ณธ์ ์ธ ๋น„๋Œ€์นญ ๊ตํ™˜์ƒํ˜ธ์ž‘์šฉ์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ๊ณ„๋ฉด ์Ÿ๋กœ์‹ ์Šคํ‚ค-๋ชจ๋ฆฌ์•ผ ์ƒํ˜ธ์ž‘์šฉ ์—๋„ˆ์ง€ ๋ฐ€๋„์˜ ์ •ํ™•ํ•˜๊ณ  ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ์ •๋Ÿ‰์  ์ธก์ •๋ฐฉ๋ฒ•์ด ์š”๊ตฌ๋œ๋‹ค. ํ•˜์ง€๋งŒ ์ธก์ •ํ•˜๋Š” ์‹œ๋ฃŒ์˜ ๊ฑฐ์น ๊ธฐ ๋ฐ ๊ณ„๋ฉด์˜ ๊ตฌ์กฐ์ ์ธ ํ˜•ํƒœ์— ๋”ฐ๋ผ์„œ ์ž์„ฑ์ธต ๊ณ„๋ฉด์—์„œ ์Šคํ•€ํŒŒ์˜ ํ˜•์„ฑ์ด ์–ด๋ ค์šด ๊ฒฝ์šฐ๊ฐ€ ์žˆ์œผ๋ฉฐ, ์ด๋Š” ์Šคํ•€ํŒŒ์˜ ์‹ ํ˜ธ ๋Œ€ ์žก์Œ๋น„ (signal-to-noise ratio, SNR)๊ฐ€ ๋‚ฎ์€ ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ์ดˆ๋ž˜ํ•œ๋‹ค. SNR์ด ๋‚ฎ์€ ๊ฒฝ์šฐ ๋‹ค์–‘ํ•œ ๋ฌผ๋ฆฌ์  ํŠน์„ฑ์„ ์ •ํ™•ํ•˜๊ฒŒ ๋ถ„์„ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ์ด๋Ÿฌํ•œ ์‹คํ—˜์  ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์ž๊ธฐ๊ด‘ํ•™ ํ•™๊ณ„์—์„œ ์ž˜ ์•Œ๋ ค์ง„ ๋ฐฉ๋ฒ•์ธ ๋ฌด๋ฐ˜์‚ฌ ์ฝ”ํŒ… (Anti-reflection coating)์„ ์ด์šฉํ•˜์—ฌ ์Šคํ•€ํŒŒ์˜ ํฌ๊ธฐ๋ฅผ ํš๊ธฐ์ ์œผ๋กœ ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. ๋ฌผ์งˆ ๊ฐ„ ๊ณ„๋ฉด์—์„œ ์ผ์–ด๋‚˜๋Š” ๋‹ค์ค‘๋ฐ˜์‚ฌ์™€ ๋ฐ˜์ „๋Œ€์นญ์ด ๊นจ์ง„ ๊ตฌ์กฐ๋ฅผ ์œ„ํ•ด ๋ฌด๋ฐ˜์‚ฌ ์ธต์œผ๋กœ MgO๋ฅผ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ๋ฌด๋ฐ˜์‚ฌ ์ฝ”ํŒ…์„ ์ด์šฉํ•˜์—ฌ ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” DM ์ƒํ˜ธ์ž‘์šฉ ์—๋„ˆ์ง€ ๋ฐ€๋„ ์ธก์ •์„ ์‹คํ—˜์ ์œผ๋กœ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ƒ์˜จ์—์„œ ์™ธ๋ถ€ ์ž๊ธฐ์žฅ ์ธ๊ฐ€์—†์ด ๊ฐ„๋‹จํ•œ ๊ตฌ์กฐ์˜ ์ž์„ฑ๋ฐ•๋ง‰์—์„œ ์ž๊ธฐ ์Šค์ปค๋ฏธ์˜จ์„ ํ˜•์„ฑํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๊ณ , ์ž๊ธฐ ์Šค์ปค๋ฏธ์˜จ ์ƒ์„ฑ๊ณผ ๊ด€๋ จํ•˜์—ฌ ์ฃผ์š”ํ•œ ๋ฌผ์งˆ์˜ ํŠน์„ฑ์ธ DM ์ƒํ˜ธ์ž‘์šฉ ์—๋„ˆ์ง€ ๋ฐ€๋„์˜ ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” ๊ฒฐ์ •๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด ๊ฒฐ๊ณผ๋Š” ํ˜„์žฌ ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋Š” ์Šคํ•€๊ถค๋„ ๋Œ๋ฆผํž˜ ๊ธฐ๋ฐ˜ ์ž๊ธฐ๋ฉ”๋ชจ๋ฆฌ ์†Œ์ž์™€ ์ž๊ธฐ ์Šค์ปค๋ฏธ์˜จ ๊ธฐ๋ฐ˜ ์†Œ์ž์˜ ์‘์šฉ์—ฐ๊ตฌ์— ๋„์›€์ด ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Yโ… . Introduction 1 โ…ก. Theoretical backgrounds 10 2.1 Exchange interaction 10 2.1.1 Heisenberg exchange interaction 10 2.1.2 Dzyaloshinskii-Moriya interaction 13 2.2 Magnetic anisotropy energy 15 2.2.1 Magneto-crystalline anisotropy 15 2.2.2 Shape magnetic anisotropy 17 2.2.3 Interfacial magnetic anisotropy 20 2.2.4 Perpendicular magnetic anisotropy 21 2.3 The other magnetic energies 23 2.3.1 Zeeman energy 24 2.3.2 Landau-Lifshitz-Gilbert equation 24 2.4 Spin wave 28 2.4.1 Basic concept of spin wave 28 2.4.2 Spin waves in the single magnetic thin film 33 2.5 Inelastic light scattering 37 2.5.1 Mechanism of inelastic scattered light 37 2.5.2 Brillouin light scattering 39 โ…ข. Fabrication process and Experiment tools 42 3.1 Sample fabrication process 42 3.1.1 Magnetic multilayer deposition 42 3.1.2 Lithography techniques 45 3.1.3 Fabrication patterning process 47 3.2 Brillouin light scattering spectroscopy 49 3.2.1 Fabry-Pรฉrot interferometer 49 3.2.2 Tandem Fabry-Pรฉrot interferometer (TFPI) 55 3.2.3 Setup of Brillouin light scattering spectroscopy system 58 3.3 Magneto-optical Kerr effect 63 3.3.1 Background of Magneto-optical Kerr effect 63 3.3.2 Setup of Laser-Magneto-optical Kerr effect system 67 3.3.3 Automatically controlled z-axis stage for Wedge-type thin films 68 3.3.4 Customized sample holder with printed circuit board 70 3.4 SQUID-VSM measurements 71 โ…ฃ. Result and discussion 1 73 4.1 Overview 73 4.2 Introduction 74 4.3 Sample fabrication and Experimental details 76 4.4 Experimental results and discussion 82 4.5 Find to nucleate the magnetic skyrmion state 89 4.5.1 Approach for numerical calculation 89 4.5.2 Approach for micromagnetic simulation 92 4.6 Summary 95 โ…ค. Result and discussion 2 97 5.1 Overview 97 5.2 Introduction 98 5.3 Physical mechanism and experimental details 101 5.4 Experimental results and discussion 106 5.5 Summary 114 โ…ฅ. Conclusion 115 โ…ฆ. References 117 ๊ตญ๋ฌธ์š”์•ฝ 130DoctordCollectio

    PM<sub>2.5</sub> Concentration Forecasting Using Weighted Bi-LSTM and Random Forest Feature Importance-Based Feature Selection

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    Particulate matter (PM) in the air can cause various health problems and diseases in humans. In particular, the smaller size of PM2.5 enable them to penetrate deep into the lungs, causing severe health impacts. Exposure to PM2.5 can result in respiratory, cardiovascular, and allergic diseases, and prolonged exposure has also been linked to an increased risk of cancer, including lung cancer. Therefore, forecasting the PM2.5 concentration in the surrounding is crucial for preventing these adverse health effects. This paper proposes a method for forecasting the PM2.5 concentration after 1 h using bidirectional long short-term memory (Bi-LSTM). The proposed method involves selecting input variables based on the feature importance calculated by random forest, classifying the data to assign weight variables to reduce bias, and forecasting the PM2.5 concentration using Bi-LSTM. To compare the performance of the proposed method, two case studies were conducted. First, a comparison of forecasting performance according to preprocessing. Second, forecasting performance between deep learning (long short-term memory, gated recurrent unit, and Bi-LSTM) and conventional machine learning models (multi-layer perceptron, support vector machine, decision tree, and random forest). In case study 1, The proposed method shows that the performance indices (RMSE: 3.98%p, MAE: 5.87%p, RRMSE: 3.96%p, and R2:0.72%p) are improved because weights are given according to the input variables before the forecasting is performed. In case study 2, we show that Bi-LSTM, which considers both directions (forward and backward), can effectively forecast when compared to conventional models (RMSE: 2.70, MAE: 0.84, RRMSE: 1.97, R2: 0.16). Therefore, it is shown that the proposed method can effectively forecast PM2.5 even if the data in the high-concentration section is insufficient

    Architectural Supports to Protect OS Kernels from Code-Injection Attacks and Their Applications

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    The kernel code injection is a common behavior of kernel-compromising attacks where the attackers aim to gain their goals by manipulating an OS kernel. Several security mechanisms have been proposed to mitigate such threats, but they all suffer from non-negligible performance overhead. This article introduces a hardware reference monitor, called Kargos, which can detect the kernel code injection attacks with nearly zero performance cost. Kargos monitors the behaviors of an OS kernel from outside the CPU through the standard bus interconnect and debug interface available with most major microprocessors. By watching the execution traces and memory access events in the monitored target system, Kargos uncovers attempts to execute malicious code with the kernel privilege. On top of this, we also applied the architectural supports for Kargos to the detection of ROP attacks. KS-Stack is the hardware component that builds and maintains the shadow stacks using the existing supports to detect this ROP attacks. According to our experiments, Kargos detected all the kernel code injection attacks that we tested, yet just increasing the computational loads on the target CPU by less than 1% on average. The performance overhead of the KS-Stack was also less than 1%

    Fast Hardware-Software Coverification by Optimistic Execution of Real Processor

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    To achieve fast verification of the software part of embedded system, we propose to run the target processor optimistically, which effectively reduces the synchronization overhead with other simulators. For the optimistic processor execution, we present a processor execution platform and state saving/restoration methods. We performed optimistic execution of ARM710A processor in the coverification of an IS-95 CDMA cellular phone system and obtained up to orders of magnitude higher performance compared with the case that the processor runs conservatively. 1. Introduction Verification of system functionality and timing is one of the most difficult and important aspects of System on a Chip (SoC) design. For many system design teams, verification takes 50% to 80% of the overall system design effort [1]. For fast verification of the hardware part of the system being designed, cycle-based simulators and high performance emulation systems have been widely used. For the verification of the s..
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