118 research outputs found
์คํ์ ์ ๊ฑฐ๊ธฐ์ ์ ์ ์ ์ด ๋ฑํ๊ธฐ์ ๋ณด์ฐ-๋ ์ดํธ ์์ ๊ฒ์ถ๊ธฐ๋ฅผ ํ์ฉํ ์์ ๊ธฐ ์ค๊ณ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2021.8. ์ผ์ ์.In this thesis, designs of high-speed, low-power wireline receivers (RX) are explained. To be specific, the circuit techniques of DC offset cancellation, merged-summer DFE, stochastic Baud-rate CDR, and the phase detector (PD) for multi-level signal are proposed.
At first, an RX with adaptive offset cancellation (AOC) and merged summer decision-feedback equalizer (DFE) is proposed. The proposed AOC engine removes the random DC offset of the data path by examining the random data stream's sampled data and edge outputs. In addition, the proposed RX incorporates a shared-summer DFE in a half-rate structure to reduce power dissipation and hardware complexity of the adaptive equalizer. A prototype chip fabricated in 40 nm CMOS technology occupies an active area of 0.083 mm2. Thanks to the AOC engine, the proposed RX achieves the BER of less than 10-12 in a wide range of data rates: 1.62-10 Gb/s. The proposed RX consumes 18.6 mW at 10 Gb/s over a channel with a 27 dB loss at 5 GHz, exhibiting a figure-of-merit of 0.068 pJ/b/dB.
Secondly, a 40 nm CMOS RX with Baud-rate phase-detector (BRPD) is proposed. The RX includes two PDs: the BRPD employing the stochastic technique and the BRPD suitable for multi-level signals. Thanks to the Baud-rate CDRโs advantage, by not using an edge-sampling clock, the proposed CDR can reduce the power consumption by lowering the hardware complexity. Besides, the proposed stochastic phase detector (SPD) tracks an optimal phase-locking point that maximizes the vertical eye opening. Furthermore, despite residual inter-symbol interference, proposed BRPD for multi-level signal secures vertical eye margin, which is especially vulnerable in the multi-level signal. Besides, the proposed BRPD has a unique lock point with an adaptive DFE, unlike conventional Mueller-Muller PD. A prototype chip fabricated in 40 nm CMOS technology occupies an active area of 0.24 mm2. The proposed PAM-4 RX achieves the bit-error-rate less than 10-11 in 48 Gb/s and the power efficiency of 2.42 pJ/b.๋ณธ ๋
ผ๋ฌธ์ ๊ณ ์, ์ ์ ๋ ฅ์ผ๋ก ๋์ํ๋ ์ ์ ์์ ๊ธฐ์ ์ค๊ณ์ ๋ํด ์ค๋ช
ํ๊ณ ์๋ค. ๊ตฌ์ฒด์ ์ผ๋ก ๋งํ๋ฉด, ์คํ์
์์, ๋ณํฉ๋ ์๋จธ๋ฅผ ์ฌ์ฉํ๋ ๊ฒฐ์ ํผ๋๋ฐฑ ๋ฑํ๊ธฐ ๊ธฐ์ , ํ๋ฅ ์ ๋ณด์ฐ ๋ ์ดํธ ํด๋ญ๊ณผ ๋ฐ์ดํฐ ๋ณต์๊ธฐ, ๊ทธ๋ฆฌ๊ณ ๋ค์ค ๋ ๋ฒจ ์ ํธ์ ์ ํฉํ ์์ ๊ฒ์ถ๊ธฐ๋ฅผ ์ ์ํ๋ค.
์ฒซ์งธ๋ก, ์ ์ ์คํ์
์ ๊ฑฐ ๋ฐ ๋ณํฉ๋ ์๋จธ๋ฅผ ์ฌ์ฉํ๋ ๊ฒฐ์ ํผ๋๋ฐฑ ๋ฑํ๊ธฐ๋ฅผ ๊ฐ์ถ ์์ ๊ธฐ๋ฅผ ์ ์ํ๋ค. ์ ์๋ ์ ์ ์คํ์
์ ๊ฑฐ ์์ง์ ์์์ ๋ฐ์ดํฐ ์คํธ๋ฆผ์ ์ํ๋ง ๋ฐ์ดํฐ, ์์ง ์ถ๋ ฅ์ ๊ฒ์ฌํ์ฌ ๋ฐ์ดํฐ ๊ฒฝ๋ก ์์ ์คํ์
์ ์ ๊ฑฐํ๋ค. ๋ํ ํํ ๋ ์ดํธ ๊ตฌ์กฐ์ ๋ณํฉ๋ ์๋จธ๋ฅผ ์ฌ์ฉํ๋ ๊ฒฐ์ ํผ๋๋ฐฑ ๋ฑํ๊ธฐ๋ ์ ๋ ฅ์ ์ฌ์ฉ๊ณผ ํ๋์จ์ด์ ๋ณต์ก์ฑ์ ์ค์ธ๋ค. 40 nm CMOS ๊ธฐ์ ๋ก ์ ์๋ ํ๋กํ ํ์
์นฉ์ 0.083 mm2 ์ ๋ฉด์ ์ ๊ฐ์ง๋ค. ์ ์ ์คํ์
์ ๊ฑฐ๊ธฐ ๋๋ถ์ ์ ์๋ ์์ ๊ธฐ๋ 10-12 ๋ฏธ๋ง์ BER์ ๋ฌ์ฑํ๋ค. ๋ํ ์ ์๋ ์์ ๊ธฐ๋ 5GHz์์ 27 dB์ ๋ก์ค๋ฅผ ๊ฐ๋ ์ฑ๋์์ 10 Gb/s์ ์๋์์ 18.6 mW๋ฅผ ์๋นํ๋ฉฐ 0.068 pJ/b/dB์ FoM์ ๋ฌ์ฑํ์๋ค.
๋๋ฒ์งธ๋ก, ๋ณด์ฐ ๋ ์ดํธ ์์ ๊ฒ์ถ๊ธฐ๊ฐ ์๋ 40 nm CMOS ์์ ๊ธฐ๊ฐ ์ ์๋์๋ค. ์์ ๊ธฐ์๋ ๋๊ฐ์ ๋ณด์ฐ ๋ ์ดํธ ์์ ๊ฒ์ถ๊ธฐ๋ฅผ ํฌํจํ๋ค. ํ๋๋ ํ๋ฅ ๋ก ์ ๊ธฐ๋ฒ์ ์ฌ์ฉํ๋ ๋ณด์ฐ ๋ ์ดํธ ์์ ๊ฒ์ถ๊ธฐ์ด๋ค. ๋ณด์ฐ ๋ ์ดํธ ํด๋ญ ๋ฐ์ดํฐ ๋ณต์๊ธฐ์ ์ฅ์ ๋๋ถ์ ์์ง ์ํ๋ง ํด๋ญ์ ์ฌ์ฉํ์ง ์์์ผ๋ก์ ํ์์ ์๋ชจ์ ํ๋์จ์ด์ ๋ณต์ก์ฑ์ ์ค์๋ค. ๋ํ ํ๋ฅ ์ ์์ ๊ฒ์ถ๊ธฐ๋ ์์ง ์์ด ์คํ๋์ ์ต๋ํํ๋ ์ต์ ์ ์์ ์ง์ ์ ์ฐพ์ ์ ์์๋ค. ๋ค๋ฅธ ์์ ๊ฒ์ถ๊ธฐ๋ ๋ค์ค ๋ ๋ฒจ ์ ํธ์ ์ ํฉํ ๋ฐฉ์์ด๋ค. ์ฌ๋ณผ ๊ฐ ๊ฐ์ญ์ด ๋ค์ค ๋ ๋ฒจ ์ ํธ์ ๋งค์ฐ ์ทจ์ฝํ ๋ฌธ์ ๊ฐ ์๋๋ผ๋ ์ ์๋ ๋ค์ค ๋ ๋ฒจ ์ ํธ์ฉ ๋ณด์ฐ ๋ ์ดํธ ์์ ๊ฒ์ถ๊ธฐ๋ ์์ง ์์ด ๋ง์ง์ ํ๋ณดํ๋ค. ๊ฒ๋ค๊ฐ ์ ์๋ ๋ณด์ฐ ๋ ์ดํธ ์์ ๊ฒ์ถ๊ธฐ๋ ๊ธฐ์กด์ ๋ฎฌ๋ฌ-๋ฎ๋ฌ ์์ ๊ฒ์ถ๊ธฐ์ ๋ฌ๋ฆฌ ์ ์ํ ๊ฒฐ์ ํผ๋๋ฐฑ ๋ฑํ๊ธฐ๊ฐ ์๋๋ผ๋ ์ ์ผํ ๋ฝ ์ง์ ์ ๊ฐ๋๋ค. ํ๋กํ ํ์
์นฉ์ 0.24mm2์ ๋ฉด์ ์ ๊ฐ์ง๋ค. ์ ์๋ PAM-4 ์์ ๊ธฐ๋ 48 Gb/s์ ์๋์์ 10-11 ๋ฏธ๋ง์ BER์ ๊ฐ์ง๊ณ , 2.42 pJ/b์ FoM์ ๊ฐ์ง๋ค.CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 THESIS ORGANIZATION 5
CHAPTER 2 BACKGROUNDS 6
2.1 BASIC ARCHITECTURE IN SERIAL LINK 6
2.1.1 SERIAL COMMUNICATION 6
2.1.2 CLOCK AND DATA RECOVERY 8
2.1.3 MULTI-LEVEL PULSE-AMPLITUDE MODULATION 10
2.2 EQUALIZER 12
2.2.1 EQUALIZER OVERVIEW 12
2.2.2 DECISION-FEEDBACK EQUALIZER 15
2.2.3 ADAPTIVE EQUALIZER 18
2.3 CLOCK RECOVERY 21
2.3.1 2X OVERSAMPLING PD ALEXANDER PD 22
2.3.2 BAUD-RATE PD MUELLER MULLER PD 25
CHAPTER 3 AN ADAPTIVE OFFSET CANCELLATION SCHEME AND SHARED SUMMER ADAPTIVE DFE 28
3.1 OVERVIEW 28
3.2 AN ADAPTIVE OFFSET CANCELLATION SCHEME AND SHARED-SUMMER ADAPTIVE DFE FOR LOW POWER RECEIVER 31
3.3 SHARED SUMMER DFE 37
3.4 RECEIVER IMPLEMENTATION 42
3.5 MEASUREMENT RESULTS 45
CHAPTER 4 PAM-4 BAUD-RATE DIGITAL CDR 51
4.1 OVERVIEW 51
4.2 OVERALL ARCHITECTURE 53
4.2.1 PROPOSED BAUD-RATE CDR ARCHITECTURE 53
4.2.2 PROPOSED ANALOG FRONT-END STRUCTURE 59
4.3 STOCHASTIC PHASE DETECTION PAM-4 CDR 64
4.3.1 PROPOSED STOCHASTIC PHASE DETECTION 64
4.3.2 COMPARISON OF THE STOCHASTIC PD WITH SS-MMPD 70
4.4 PHASE DETECTION FOR MULTI-LEVEL SIGNALING 73
4.4.1 PROPOSED BAUD-RATE PHASE DETECTOR FOR MULTI-LEVEL SIGNAL 73
4.4.2 DATA LEVEL AND DFE COEFFICIENT ADAPTATION 79
4.4.3 PROPOSED PHASE DETECTOR 84
4.5 MEASUREMENT RESULT 88
4.5.1 MEASUREMENT OF THE PROPOSED STOCHASTIC BAUD-RATE PHASE DETECTION 94
4.5.2 MEASUREMENT OF THE PROPOSED BAUD-RATE PHASE DETECTION FOR MULTI-LEVEL SIGNAL 97
CHAPTER 5 CONCLUSION 103
BIBLIOGRAPHY 105
์ด ๋ก 109๋ฐ
๋คํ์ฅ ๊ด์ธก ์๋ฃ๋ฅผ ์ด์ฉํ ๋ค์ํ ํ๊ฒฝ์์์ ์ํ ์งํ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ ์์ฐ๊ณผํ๋ํ ๋ฌผ๋ฆฌยท์ฒ๋ฌธํ๋ถ, 2017. 8. ์ด๋ช
๊ท .์ํ์ ํํ์ ๋ฌผ๋ฆฌ์ ์ธ ํน์ฑ๋ค์ ์ํ๊ฐ ์ํ ํ๊ฒฝ์ ๋ฐ๋ผ ๋ฌ๋ผ์ง๋ค. ๊ทธ๋ฌ๋ฏ๋ก ์ ํ์ ์งํ๋ฅผ ์ดํดํ๊ธฐ ์ํด์๋ ์ํ์ ์์ฉํ๋ ํ๊ฒฝ ํจ๊ณผ์ ๋ํ ์ดํด๊ฐ ๋๋ฐ๋์ด์ผ ํ๋ค. ๋ณธ ํ์๋
ผ๋ฌธ์ ๋ค์ํ ํ๊ฒฝ์ ์์นํ๋ ์ํ๋ค์ ๋คํ์ฅ(0.2 โ 25 ฮผm) ํน์ฑ๊ณผ ํ๊ฒฝ ํจ๊ณผ์ ๋ํ ๋ค ํธ์ ์ฐ๊ตฌ๋ค๋ก ๊ตฌ์ฑ๋์ด ์๋ค.
2์ฅ์์ ์ฐ๋ฆฌ๋ SDSS์ WISE ์๋ฃ๋ฅผ ์ด์ฉํ Abell 2199 ์ด์ํ๋จ์ ๊ดํ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ด์ผ๊ธฐํ๋ค. Abell 2199 ์ด์ํ๋จ์ ์ฌ๋ฌ ์ํ๋จ๊ณผ ์ํ๊ตฐ์ด ๋ชจ์ฌ์๋, ๊ฐ๊น์ด ์ฐ์ฃผ(z โผ 0.03)์์ ๊ฐ์ฅ ๋ฌด๊ฑฐ์ด ์ํ ๊ตฌ์กฐ์ด๋ค. ์ฐ๋ฆฌ๋ Abell 2199 ์ด์ํ๋จ์ ์์๋ ์ํ๋ค์ ์ค์ ์ธ์ (MIR) ์์ง์-๊ด๋ ๋ถํฌ๋ก๋ถํฐ, ์ํ์ ์ค๊ฐ ์งํ ๋จ๊ณ์ ํด๋นํ ๋ MIR Green Valley(MGV)๋ฅผ ์ ์ํ์๋ค. MGV์ ์๋ ์ํ๋ค์ ๊ฐ์๊ด ์๋ฑ๊ธ ๋์์๋ Red Sequence์ ์์นํ๋ค. MGV ์ํ๋ค์ Red Sequence์ ํจ๊ป ์์นํ๋ MIR Blue ์ํ๋ค๊ณผ ๋น์ทํ Dn4000๊ณผ ๋ณํ์ฑ๋ฅ (Star Formation Rates)๋ฅผ ๊ฐ๋๋ค. ํญ ์ฑ ์ข
์กฑ ๋ชจํ(stellar population model)์ด ์์ธกํ๋ MGV ์ํ์ ์ํ ๋ณ๋ค์ ํ๊ท ๋์ด๋ 10โ100์ต๋
์ผ๋ก, MIR Blue ์ํ๋ค(> 100์ต๋
)๋ณด๋ค ์๋์ ์ผ๋ก ์ ๋ค. ์ด๋ฌํ ๊ฒฐ๊ณผ๋ค์ ์ํ๊ฐ ๋ณํ์ฑ ํ๋์ด ๊ฑฐ์ ์์ ํ ๋ฉ์ถ ๋ค์ MGV๋ก ์ง์
ํ๋ฉฐ, MGV์์ ์์ญ์ต๋
์ ์๊ฐ์ ๋ณด๋ธ ๋ค์ MIR Blue ์ํ๋ก ์งํํ๋ค๋ ๊ฒ์ ๋งํด์ค๋ค. ์ํ์ ํ ํ๋ฅผ ์ดํด๋ณด์์ ๋, MGV์๋ ์กฐ๊ธฐํ ์ํ์ ๋ง๊ธฐํ ์ํ๊ฐ ๋น์ทํ ๋น์จ๋ก ์กด์ฌํ๋ค. MGV ์กฐ๊ธฐํ ์ํ๋ MGV ๋ง๊ธฐํ ์ํ๋ณด๋ค ๊ณ ๋ฐ๋ ํ๊ฒฝ(์ํ๋จ/์ํ๊ตฐ์ ์ค์ฌ๋ถ)์ ์ง์ค๋์ด ์๋ค. ์ด๋ ์ํ๋จ๊ณผ ์ํ๊ตฐ ์ค์ฌ๋ถ ํ๊ฒฝ์ด MGV ๋ง๊ธฐํ ์ํ์์ MIR ์กฐ๊ธฐ ํ ์ํ๋ก์ ํํ ๋ณํ์ ์ํฅ์ ์ฃผ์์์ ์๋ฏธํ๋ ๊ด์ธก์ ์ธ ์ฆ๊ฑฐ์ด๋ค. MGV ๋ง๊ธฐํ ์ํ๊ฐ ๋งค์ฐ ๋ฎ์ ๋ณํ์ฑ ํ๋์ ๋ณด์์๋ ๋ถ๊ตฌํ๊ณ ๊ทธ ํํ๋ฅผ ์ ์งํ๊ณ ์๋ค๋ ๊ฒ์ MGV ๋ง๊ธฐํ ์ํ์ ๋ณํ์ฑ๋ฅ ๋ณํ์ ๋ฏธ์น๋ ํ๊ฒฝ ํจ๊ณผ๊ฐ ๊ธด ์๊ฐ์ ๊ฑธ์ณ ์ฒ์ฒํ ์์ฉ ํ๋ค๋ ๊ฒ์ ๋งํด์ค๋ค. ๊ทธ๋ฌ๋ฏ๋ก MGV ๋ง๊ธฐํ ์ํ๋ฅผ ํ์ฑํ๋๋ฐ ๊ธฐ์ฌํ ํ๊ฒฝ ํจ๊ณผ๋ Strangulation ํน์ Starvation์ผ ๊ฐ๋ฅ์ฑ์ด ๋๋ค. ๋ฐ๋ฉด, MGV ๋ง๊ธฐํ ์ํ์์ MGV ์กฐ๊ธฐํ ์ํ๋ก์ ํํ ๋ณํ๋ ์ํ๋จ/์ํ๊ตฐ ์ค์ฌ๋ถ์์ ๋น๋ฒํ ์ผ์ด๋๋ ์ํ ๋ณํฉ (merging) ๋ฐ ์กฐ์ ์ํธ์์ฉ(tidal interaction)์ ์ํ ๊ฒ์ผ๋ก ๋ณด์ธ๋ค.
์ฐ๋ฆฌ๋ 3์ฅ์์ ๋ฐ์ง์ํ๊ตฐ(Compact Groups of Galaxies)์์ ์ผ์ด๋๋ ์ํ ์ง ํ์ ๋ํ ์ฐ๊ตฌ๋ฅผ ์ด์ผ๊ธฐํ๋ค. ๋ฐ์ง์ํ๊ตฐ์ ์ฝ 100 kpc ์ดํ์ ์ข์ ๊ณต๊ฐ์ 3โ10๊ฐ์ ์ํ๋ค์ด ๋ฐ์งํด ์์ผ๋ฉฐ, ์ํ ๊ฐ์ ์ํธ์์ฉ์ด ๋งค์ฐ ํ๋ฐํ ํ๊ฒฝ์ด๋ค. ์ฐ๋ฆฌ๋ Sohn et al. (2016)์ด ๋ง๋ ๋ฐ์ง์ํ๊ตฐ ๋ชฉ๋ก๊ณผ WISE ์๋ฃ๋ฅผ ์ด์ฉํ์ฌ ๋ฐ์ง์ํ๊ตฐ์ ์ํ ์ํ๋ค์ MIR ํน์ฑ๋ค์ ์ดํด๋ณด์๋ค. ๋ฐ์ง์ํ๊ตฐ ์ํ๋ค์ ์ค์ ์ธ์ [3.4] โ [12] ์์ง ์๋ ์ํ๋จ ์ํ๋ค์ ๋นํด ํ๊ท ์ ์ผ๋ก ์๋ค. ์ด ๊ฒฐ๊ณผ๋ ๋ฐ์ง์ํ๊ตฐ์ ์์นํ ์ํ๋ค์ ํ๊ท ๋ณ ๋์ด(mean stellar age)๊ฐ ์ข ๋ ๋ง๋ค๋ ๊ฒ์ ์๋ฏธํ๋ค. ๋ํ, ๋ฐ์ง์ํ๊ตฐ์ ์ํ๋จ์ ๋นํด ๋ฎ์ MGV ์ํ ๋น์จ์ ๋ณด์ธ๋ค. ์ด๋ฌํ ์์ MGV ์ํ ๋น์จ์ ์ ๋ฐ๋ ํ๊ฒฝ์ ์์นํ ๋ฐ์ง์ํ๊ตฐ๊ณผ ๊ณ ๋ฐ๋ ํ๊ฒฝ์ ์์นํ ๋ฐ์ง์ํ๊ตฐ ๋ชจ๋์์ ๋ฐ๊ฒฌ๋๋ค. ์ด ๋ ๋ฐ์ง์ํ๊ตฐ์์ ์ํ ์งํ๊ฐ ๋น ๋ฅด๊ฒ ์งํ๋์์์ ์๋ฏธํ๋ค. ๊ณ ๋ฐ๋ ํ๊ฒฝ์ ์์นํ ๋ฐ์ง์ํ๊ตฐ์ ์ ๋ฐ๋ ํ๊ฒฝ์ ์์นํ ๋ฐ์ง์ํ๊ตฐ๋ณด๋ค ๋์ ๋น์จ์ ์กฐ๊ธฐํ ์ํ๋ค๊ณผ ์ ์ MIR ์์ง์๋ฅผ ๊ฐ๋ ์ํ๋ค๋ก ์ด๋ฃจ์ด์ ธ์๋ค. ์ด๋ ๋ฐ์ง์ํ๊ตฐ์ด ์ฃผ๋ณ ํ๊ฒฝ์ผ๋ก๋ถํฐ ๋ฉค๋ฒ ์ํ๋ฅผ ์๋กญ๊ฒ ๊ณต๊ธ๋ฐ๋๋ค๋ ๊ฒ์ ๋งํด์ฃผ๋ ๊ด์ธก์ ์ฆ๊ฑฐ์ด๋ค. ์ฐ๋ฆฌ๋ ์ด ์ฐ๊ตฌ๋ฅผ ํตํด ๋ฐ์ง์ํ๊ตฐ์ด ๋ค๋ฅธ ์ํ ํ๊ฒฝ๋ณด๋ค ์ํ ์งํ๊ฐ ๋น ๋ฅด๊ฒ ์งํ๋๋ ํ๊ฒฝ์ด๋ฉฐ, ๋ฌด๊ฑฐ์ด ํ์์ํ ํ์ฑ์ ํฌ๊ฒ ๊ธฐ์ฌํ๋ ํ๊ฒฝ์์ ๋ฐํ๋๋ค.
4์ฅ์์๋ Gemini-North ๋ง์๊ฒฝ์ GMOS IFU๋ฅผ ์ด์ฉํ E+A ์ํ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ด์ผ๊ธฐํ๋ค. E+A ์ํ ์คํํธ๋ผ์์ ๋ณด์ด๋ ๊ฐํ ๋ฐ๋จธํก์์ (Hฮณ, Hฮด, Hฮฒ)์ ์ ์ A ํ ๋ณ๋ค์ ์กด์ฌ๋ฅผ ์๋ฏธํ๋ค. ํ์ง๋ง ์ ์ ๋ณ๋ค์ด ์ฃผ๋ก ๋ถํฌํ๋ ์ํ๋ค๊ณผ๋ ๋ฌ๋ฆฌ, E+A ์ํ์์๋ Hฮฑ์ ๊ฐ์ ๋ฐฉ์ถ์ ์ด ๊ฑฐ์ ๋ณด์ด์ง ์๋๋ค. ์ด๋ฌํ ์คํํธ๋ผ์ ํน์ฑ์ E+A ์ํ๊ฐ 10์ต๋
์ ์ฏค์ ๊ฐํ ๋ณํญ๋ฐ์ ๊ฒช์์ง๋ง, ์ด๋ ํ ์ด์ ๋๋ฌธ์ ํ์ฌ๋ ๋ณํ์ฑ ํ๋์ด ๊ฑฐ์ ๋ฉ์ถ ์ํ์์ ๋งํด์ค๋ค. ๊ทธ๋ฌ๋ฏ๋ก E+A ์ํ๋ Post-Starburst ์ํ์ด๋ฉฐ, ์ํ ์งํ์ ์ค๊ฐ ๋จ๊ณ์ ์๋ ๋ํ์ ์ธ ์ํ ์ข
์กฑ์ผ๋ก ์ถ์ ๋๋ค. E+A ์ํ์ ์ ์ Aํ ๋ณ๋ค์ ๊ณต๊ฐ ๋ถํฌ๋ ๋ณํญ๋ฐ ํ์์ด ์ผ์ด๋๋ ์์ญ์ ๋ํ ์ ๋ณด๋ฅผ ์ง์ ์ ์ผ๋ก ๋ณด ์ฌ์ค๋ค. ๊ธฐ์กด์ ์์น์คํ์์๋ ์ํ ๋ณํฉ ์ ๊ฐ์ค๊ฐ ์ํ ์ค์ฌ๋ถ๋ก ๋ชจ์ฌ๋ค๊ณ , ์ํ ์ค์ฌ๋ถ๋ก๋ถํฐ ๋ฐ๊ฒฝ 1 kpc ์ด๋ด์ ์์ญ์์ ๊ฐํ ๋ณํญ๋ฐ์ด ์๊ธด๋ค๊ณ ์์ธกํ๋ค. ๊ทธ๋ฌ๋ ๊ฐ๊น์ด(0.03 < z < 0.05) ๋ค์ฏ๊ฐ์ E+A ์ํ๋ค์ ๋ํ ์ฐ๋ฆฌ์ ๊ด์ธก ๊ฒฐ๊ณผ๋ ๋ค์ฏ ์ํ ๋ชจ๋์์ Aํ ๋ณ๋ค์ด 1 kpc ์์ญ ๋ฐ๊นฅ๊น์ง ๋๊ฒ ๋ถํฌํ๊ณ ์์์ ๋ณด์ฌ์ค๋ค. ์ฐ๋ฆฌ๋ Aํ ๋ณ๋ค์ ์ํ ์ง์ค๋(central concentration)์ ๋ณํญ๋ฐ ์ธ๊ธฐ ์ฌ์ด์ ์๊ด๊ด๊ณ๋ฅผ ๋ฐ๊ฒฌ ํ์๋ค. ์ด ๊ฒฐ๊ณผ๋ Aํ ๋ณ๋ค์ ๊ณต๊ฐ ๋ถํฌ๊ฐ E+A ์ํ๊ฐ ๊ณผ๊ฑฐ์ ๊ฒช์ ๋ณํญ๋ฐ์ ํน์ฑ์ ๋ฐ๋ผ ๋ค๋ฅด๊ฒ ๋ํ๋ ์ ์์์ ๋งํด์ค๋ค.
๋ง์ง๋ง์ผ๋ก ์ฐ๋ฆฌ๋ 5์ฅ์์ ๋์ ์ํ์ ๋ง๋ ๊ตฌ์กฐ์ ํ๋์ฑ ์ํํต(Active Galactic Nuclei, AGN) ๊ฐ์ ๊ด๊ณ์ ๋ํ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ด์ผ๊ธฐํ๋ค. ์ด ์ฐ๊ตฌ์์๋ ์ฝ 9์ฒ๊ฐ์ ๋์ ์ํ๋ค๊ณผ Lee et al. (2012a)์ ๋ถ๋ฅ ๊ฒฐ๊ณผ๋ฅผ ์ด์ฉํ์๋ค. AGN๋ฅผ ๋ณด์ ํ ์ํ๋ค์ ๊ทธ๋ ์ง ์์ ์ํ๋ค์ ๋นํด 2.5๋ฐฐ ๋์ ํ๋ฅ ๋ก ๋ง๋ ๊ตฌ์กฐ๋ฅผ ๊ฐ์ง๊ณ ์์ผ๋ฉฐ, AGN ๋น ์จ๋ ๋ง๋๋ฅผ ๊ฐ์ง์ง ์์ ์ํ๋ณด๋ค ๋ง๋์ํ์์ ๋๊ฒ ๋ํ๋๋ค. ๊ทธ๋ฌ๋ ์ํ์ u โ r ์์ง์์ ๋ณ์ง๋(stellar mass)๋ฅผ ๊ณ ์ ํ๊ณ ๋น๊ตํ์ ๋๋ ์์ ๊ฒฐ๊ณผ๊ฐ ๋ฐ๊ฒฌ๋์ง ์๋ ๋ค. ๋ํ, AGN์ ํ๋ ์ธ๊ธฐ๋ฅผ ์๋ฏธํ๋ Eddington ratio๋ฅผ ๋น๊ตํ ๊ฒฐ๊ณผ ๋ง๋์ํ์ ๋ง๋๊ฐ ์๋ ์ํ ์ฌ์ด์ ํฐ ์ฐจ์ด๋ฅผ ๋ฐ๊ฒฌํ์ง ๋ชปํ์๋ค. ์ด ๊ฒฐ๊ณผ๋ค์ AGN ํ๋์ด ๋ง ๋๊ฐ ์๋ ๊ฒฝ์ฐ์๋ ๊ฐํ๊ฒ ๋ํ๋์ง ์์ผ๋ฉฐ, ๋ง๋์ ์ํด์ AGN์ ์ธ๊ธฐ๊ฐ ์ฆ๊ฐ๋์ง ์์์ ๋ณด์ฌ์ค๋ค.The role of environment in galaxy evolution is an important issue in recent astronomy. In order to better understand how environment affects galaxy transition from star-forming galaxies to quiescent galaxies, we conduct several studies using multi-wavelength data, from near-ultraviolet to mid-infrared (MIR), of nearby galaxies in various environments.
First, using the Wide-field Infrared Survey Explorer (WISE) data, we study the MIR properties of the galaxies in the A2199 supercluster at z = 0.03 to understand the star formation activity of galaxy groups and clusters in the supercluster environment. We classify the supercluster galaxies into three classes in the MIR color- luminosity diagram: MIR blue cloud (massive, quiescent and mostly early-type), MIR star-forming sequence (mostly late-type), and MIR green valley galaxies. These MIR green valley galaxies are distinguishable from the optical green valley galaxies, in the sense that they belong to the optical red sequence. We find that the fraction of each MIR class does not depend on virial mass of each group/cluster. We compare the cumulative distributions of surface galaxy number density and cluster/group-centric distance for the three MIR classes. MIR green valley galaxies show the distribution between MIR blue cloud and MIR star-forming sequence galaxies. However, if we fix galaxy morphology, early- and late-type MIR green valley galaxies show different distributions. Our results suggest a possible evolutionary scenario of these galaxies: 1) Late-type MIR star-forming sequence galaxies โ 2) Late-type MIR green valley galaxies โ 3) Early-type MIR green valley galaxies โ 4) Early-type MIR blue cloud galaxies. In this sequence, star formation of galaxies is quenched before the galaxies enter the MIR green valley, and then morphological transformation occurs in the MIR green valley.
Second, we study the MIR properties of galaxies in compact groups and their environmental dependence. We find that the MIR [3.4] โ [12] colors of compact group early-type galaxies are on average bluer than those of cluster early-type galaxies. When compact groups have both early- and late-type member galaxies, the MIR colors of the late-type members in those compact groups are bluer than the MIR colors of cluster late-type galaxies. As compact groups are located in denser regions, they tend to have larger early-type galaxy fractions and bluer mean MIR colors of member galaxies. These trends are also seen for neighboring galaxies around compact groups. However, compact group member galaxies always have larger early-type galaxy fractions and bluer MIR colors than their neighboring galaxies. Our findings suggest that the properties of compact group galaxies depend on both internal and external environments of compact groups, and that galaxy evolution is faster in compact groups than in the central regions of clusters. Furthermore, our findings suggest that there is a connection between compact group members and their neighboring galaxies, and that neighboring galaxies are sources of compact group members.
Third, we present the two-dimensional distribution of stellar population in five E+A galaxies (0.03 kpc scales. In contrast, Pracy et al. (2013) found a central concentration of A-stars and strong negative Balmer absorption line gradients within 1 kpc scales for local (z < 0.04) E+A galaxies. They claimed that previous studies failed to detect the central concentration because the E+A galaxies samples in previous studies are too far (z โผ 0.1) to resolve the central kpc scales. To check Pracy et al.s argument and the expectation from simulations, we selected five E+A galaxies at 0.03 < z < 0.05. Thanks to good seeing (โผ 0.โฒโฒ8 โ 0.7 kpc) of our observations, we are able to resolve the central 1 kpc region of our targets. We find that all five galaxies have negative Balmer line gradients, but that three galaxies have flatter gradients than those reported in Pracy et al. We discuss the results in relation with galaxy merger history.
Finally, we investigate the connection between the presence of bars and AGN activity, using a volume-limited sample of โผ9,000 late-type galaxies with axis ratio b/a > 0.6 and Mr < โ19.5 at low redshift (0.02 โค z โฒ 0.055), selected from Sloan Digital Sky Survey Data Release 7. We find that the bar fraction in AGN-host galaxies (42.6%) is โผ2.5 times higher than in non-AGN galaxies (15.6%), and that the AGN fraction is a factor of two higher in strong-barred galaxies (34.5%) than in non-barred galaxies (15.0%). However, these trends are simply caused by the fact that AGN-host galaxies are on average more massive and redder than non-AGN galaxies because the fraction of strong-barred galaxies (fSB) increases with u โ r color and stellar velocity dispersion. When u โ r color and velocity dispersion (or stellar mass) are fixed, both the excess of fSB in AGN-host galaxies and the enhanced AGN fraction in strong-barred galaxies disappears. Among AGN-host galaxies we find no strong difference of the Eddington ratio distributions between barred and non-barred systems. These results indicate that AGN activity is not dominated by the presence of bars, and that AGN power is not enhanced by bars. In conclusion we do not find a clear evidence that bars trigger AGN activity.1 Introduction 1
1.1 Environmental Effects on Galaxy Evolution 1
1.2 Why Do We Need Multi-Wavelength Data? 4
1.3 A Nearby Supercluster of Galaxies, the A2199 Supercluster 6
1.4 The Densest Environment, Compact Groups of Galaxies 8
1.5 Galaxies in Transition, E+A Galaxies 12
1.6 Properties of Barred Galaxies and Environment 15
1.7 Purpose of this Thesis 18
2 Galaxy Evolution in the MIR Green Valley: A Case of the A2199 Supercluster 21
2.1 Introduction 21
2.2 Data 24
2.3 Galaxy Groups and Clusters in the A2199 Supercluster 25
2.4 Integrated SFRs of Galaxy Systems in the A2199 Supercluster 30
2.5 MIR Properties of Galaxies in Galaxy Systems of the A2199 Supercluster 33
2.5.1 Galaxy Classification in the MIR Color-Luminosity Diagram 33
2.5.2 Galaxies in Groups/Clusters and their MIR Classes 41
2.5.3 Environmental Dependence based on ฮฃ5 and R/R200 44
2.6 Discussion 50
2.6.1 Star Formation Activity of Galaxy Systems and their Dependence on Virial Mass 50
2.6.2 Morphology Dependence of the Galaxies in the MIR Green Valley 53
2.7 Summary and Conclusions 58
3 A WISE MIR View on the Highway for Galaxy Evolution in Compact Groups 61
3.1 Introduction 61
3.2 Data 66
3.3 Results 69
3.3.1 Galaxy Color Distributions 69
3.3.2 MIR Color-Luminosity Diagram 77
3.3.3 Environments of Compact Group Galaxies 80
3.4 Discussion 93
3.4.1 Fast Galaxy Evolution in Compact Groups 93
3.4.2 Relation between Compact Group Member Galaxies and Neighboring Galaxies 99
3.4.3 Hydrodynamic Interactions in Compact Groups 101
3.5 Summary and Conclusions 103
4 A GMOS-IFU Spectroscopy of E+A Galaxies: On the Spatial Distribution of Young Stellar Population 107
4.1 Introduction 107
4.2 Observations and Data Reduction 110
4.2.1 TargetSelection 110
4.2.2 Observations 112
4.2.3 DataReduction 114
4.3 Spatial Distribution of the E+A Signatures 115
4.3.1 2D Image Construction from the 3D Datacube 115
4.3.2 Comparison with Pracy et al. (2013)s E+A Sample 120
4.4 Discussion 125
4.5 Summary and Conclusions 129
5 Do Bars Trigger AGN Activity? 131
5.1 Introduction 131
5.2 Data and Methods 134
5.2.1 SDSS Galaxy Sample 134
5.2.2 Classification of Spectral Types 136
5.3 Results 138
5.3.1 Dependence of Bar Fraction on AGN Activity 139
5.3.2 Dependence of AGN Fraction on Bar Presence 145
5.3.3 Comparison of Eddington Ratio between Barred and Non-barred AGN-host Galaxies 151
5.4 Discussion 156
5.4.1 Do AGNs Favor Barred Galaxies? 156
5.4.2 Dependence on the MBHโฯ Relation 157
5.4.3 What Triggers AGNs? 160
5.5 Summary and Conclusions 163
6 Summary and Conclusions 165
Bibliography 169
์์ฝ 195Docto
ํ๊ตญ์ด ์ฐ์์ด ์ฌ์ ๊ตฌ์ถ์ ์ํ ์ํ์ ์ฐ๊ตฌ
The aim of this paper is to find a methodology for building a Korean associative thesaurus. Associative words are believed to be a vocabulary which frequently co-occurs on a account of their meaning adjacency. Associative words belong to unconscious implicit human memory. Therefore for identifying the substantiality of associative words, it is said to be more efficient to collect them by corpus based approach which can catch frequently co-occurring words rather than to collect them by questionnaire which asks conscious reflection.
On this point of view, this paper attempts to extract associative words from adjacent words to a target word in the corpus. It shows that 75% of associative words collected by questionnaire appear in the adjacent word list, which is made by extracting left and right three words adjacent to a target word. This method also catches associative words extensively, which are not included by questionnaire. It means that corpus based approach to Korean associative thesaurus can be more efficient and more economical. A underlying methodology for building a Korean associative thesaurus is established
A Quantitative Research on Productivity of Derivational affix in Korean
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) --์์ธ๋ํ๊ต ๋ํ์ :๊ตญ์ด๊ตญ๋ฌธํ๊ณผ,2007.Docto
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