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應用於相對生物效應及微電子可靠度測試的輻射品質評估方法

為了解決Fission SV的問題，作者江悅 這樣論述：

Table of Contents摘要 iiiAbstract ivChapter 1. Introduction 1Chapter 2. Radiation Environments and Their Quality 72.1. RADIATION QUANTITY AND QUALITY 72.2. RADIATION ENVIRONMENT IN THIS STUDY 92.2.1. Radiation for semiconductor industrial practice 102.2.2. Radia

tion for medical practice 122.3. SUMMARY 20Chapter 3. Microdosimetry and its simulation and measurement 213.1. CONCEPTS OF MICRODOSIMETRY 243.2. MONTE CARLO SIMULATION 353.3. MICRODOSIMETRY MEASUREMENT 403.4. SUMMARY 46Chapter 4. Lineal energy of proton in s

ilicon 474.1. THE DIFFERENCE BETWEEN LINEAL ENERGY AND LET 474.2. MICRODOSIMETRY SIMULATION 534.3. RESULTS AND DISCUSSIONS 574.3.1. Effect of SV thickness on y distribution 574.3.2. Lineal energy contribution from various secondary species 634.3.3. Effect of vario

us physics models on secondary yields 694.4. SUMMARY 70Chapter 5. Equivalence of Neutrons and Protons in Single Event Effects Testing 725.1. SINGLE EVENT EFFECT TESTING – METHODS AND FACILITIES 725.2. PROCESS FOR EQUIVALENCE VALIDATION 755.2.1. Monte Carlo Simulation

775.2.2. Material Structure 795.2.3. Data Analysis 815.3. RESULTS AND DISCUSSIONS 825.3.1. LET Difference between Neutrons and Protons 825.3.2. Secondary Particle Yield Difference between Neutronand Proton 885.3.3. LET Difference between Layer Structures with andwit

hout SiGe 915.3.4. Secondary Particle Yields Difference between Layer Structure with and without SiGe 935.3.5. Energy Deposition Difference between Neutronsand Protons 955.4. SUMMARY 99Chapter 6. Silicon equivalent gas in silicon equivalent proportional counter 1016.1.

SILICON EQUIVALENT GAS 1016.2. SIMULATION AND ANALYZATION METHODS FOR SE GAS SELECTION 1036.3. RESULTS AND DISCUSSION 1046.3.1. LET spectra 1046.3.2. Secondary particle yields 1056.4. SUMMARY 112Chapter 7. High Z material enhanced RBE 1147.1. RADIATION SENSI

TIZERS IN RADIATION THERAPY 1147.2. RBE SIMULATION AND CALCULATION METHODS 1177.2.1. MKM simulation 1177.2.2. DSB simulation 1207.3. RESULTS AND DISCUSSION 1217.3.1. Verification for microdosimetry simulation 1217.3.2. Microdosimetry spectra and RBE 1237.3.3.

Secondary electron spectra 1307.3.4. Correlation of DSB with electron energy 1327.3.5. Spectra of DSB 1337.4. SUMMARY 134Chapter 8. Conclusion 136References 138 List of FiguresFigure 1 1 LET threshold of SEEs vs. Feature size [6] 4Figure 1 2 (a) mechanism of total

ionization effect, (b) ΔVtm vs. time diagram due to TID [4] 4Figure 1 3 (a) Ionizing radiation generates charge, (b) Negative charge moves to the positive electrode to generate current, (c) Potential difference generates current, and (d) Current vs. time diagram due to a single event under revers

e bias[7] 5Figure 2 1 Example of a CMOS structure and mechanism of single event effect. (A) is the event from the heavy ions. (B) from the natural particle or proton. 12Figure 2 2 Schematic comparison of the local dose distributions (left) and corresponding spatial DSB distributions (right) fo

r low energetic (top) and high energetic (bottom) carbon ions. Assumed DSB yields are 50 DSB and 0.5 DSB for the low energetic and high energetic ions, respectively [33] 18Figure 2 3 Representation of a 10mGy dose delivered from gamma 60Co (left) and the same dose delivered by 1 MeV neutrons (rig

ht) in a cell volume of 150 cell of 5 µm diameter [37] 19Figure 2 4　The explanation of domain in microdosimetry kinetic model 19Figure 3 1 Specific energy (dE/dm) deposited by radiation in matter as a function of mass with the macroscopic dose being constant. 23Figure 3 2 lineal energy dist

ribution of tissue irradiated by 250 kVp X-ray. Linear scale. 31Figure 3 3 lineal energy distribution of tissue irradiated by 250 kVp X-ray. Log scale. 32Figure 3 4 lineal energy distribution of tissue irradiated by 250 kVp X-ray. Semi-log scale. 33Figure 3 5 dose weighted lineal energy dis

tribution of tissue irradiated by 250 kVp X-ray. Semi-log scale. 34Figure 3 6 Lineal energy spectra of different sensitive volumes in silicon irradiated by a 200 MeV proton beam. 35Figure 3 7 Block diagram of basic process of Monte Carlo method in radiation transportation code 39Figure 3 8

A sketch of the cross-sectional view of SEPC with its component 43Figure 3 9 block diagram of the SEPC measurement system 43Figure 3 10 Simulated lineal energy spectra for SEPC irradiated by 50 kVp and 150 kVp X-ray 45Figure 4 1 The geometry setup in this study. The silicon is with natural

isotope abudence, density is 2,330 mg/cm3 and mean excitation potential I = 173 eV. 57Figure 4 2 Lineal energy spectra of different sensitive volumes in silicon irradiated by a 200 MeV proton beam. 61Figure 4 3 Cumulative distribution function of kinetic energy of secondary particles generate

d by 200 protons irradiated on silicon 62Figure 4 4 Lineal energy spectra of different sensitive volumes in silicon irradiated by a 200 MeV proton beam (log y scale). 63Figure 4 5 y spectra in 100 nm silicon irradiated by a 200 MeV proton beam 67Figure 4 6 Secondary particle yields in 100 n

m silicon irradiated by a 200 MeV proton beam using various physics models. BIC represents the Binary cascade model. BERT represents Bertini cascade model. HP represents high precision add-on 70Figure 5 1 The Los Alamos Neutron Science Centre (LANSCE) broad band neutron spectrum used in this stud

y [112]. 76Figure 5 2 The layer structure (a) without SiGe and (b) with SiGe used in this simulation (not to scale). 80Figure 5 3 Linear energy transfer (LET) spectra in a structure without silicon-germanium (SiGe) irradiated by 63, 105, 150, 200, and 230 MeV proton and LANSCE neutron. 84Fi

gure 5 4 The LET contribution from He, Mg, and Al generated by the 200 MeV proton and the LANSCE neutron. In parentheses, the first symbol represents incident particles, and the second symbol represents particles that contribute to the LET. 85Figure 5 5 LET spectra in a structure without SiGe fro

m 10, 30, 50, 63, and 200 MeV protons and LANSCE neutron. 86Figure 5 6 The secondary particle yields in structure without SiGe irradiated by 63, 105, 150, 200, and 230 MeV protons and LANSCE neutron. 89Figure 5 7 LET spectra of the structure with and without SiGe irradiated by 63 and 230 MeV p

rotons and LANSCE neutron. The plot is in log-log scale. 92Figure 5 8 The secondary particle yields in the structure with and without SiGe irradiated by 63 and 230 MeV protons and LANSCE neutron. 94Figure 6 1 Simulated LET spectra in the SEPC cavity for proton irradiations of (a) 63 MeV and (b

) 230 MeV. Results of cavity gas Si, CCl4, propane, Ne and Ar are plotted. 107Figure 6 2 Simulated LET spectra in the SEPC cavity for neutron irradiations of (a) 4.44 MeV and (b) 750 MeV. Results of cavity gas Si, CCl4, propane, Ne and Ar are plotted. 108Figure 6 3 Evaluation index, EI, of LET

spectra for different SEPC cavity gases under proton and neutron irradiations 109Figure 6 4 Simulated secondary particle yields in the SEPC cavity for proton irradiations of (a) 63 MeV and (b) 230 MeV. Results of cavity gas Air, Ar, CCl4, CO2, He, Kr, Ne, propane, Si and Xe are plotted 110Fig

ure 6 5 Simulated secondary particle yields in the SEPC cavity for neutron irradiations of (a) 4.44 MeV and (b) 750 MeV. Results of cavity gas Air, Ar, CCl4, CO2, He, Kr, Ne, propane, Si and Xe are plotted. 111Figure 6 6 Evaluation index, EI, of secondary particle yields for different SEPC cavity

gases under proton and neutron irradiations 112Figure 7 1 Input spectra for Monte Carlo simulation. The spectra are measured by INER and modified for Geant4 GPS input format. 119Figure 7 2 Comparison of simulation data with measurement data. Dots represent the simulation data with 20 points p

er decade. The continuous line shows the measurement data in INER’s medium energy X-ray air kerma rate calibration system. 123Figure 7 3 Microdosimetry spectra of 80 kVp La transmission X-ray w/ and w/o iodine 125Figure 7 4 Microdosimetry spectra of 250 kVp X-ray w/ and w/o iodine 126Figure

7 5 Secondary electron spectra. (A) The secondary electron of 80 kVp La Fluorescence X-ray. (B) The secondary electron of 250 kVp X-ray 131Figure 7 6 The yields of DSB for different electron. The energy step is set 20 energies per decade in log scale in simulation. The cubic spline method is app

lied to do the interpolation. The fitting curve is shown in 10 eV per step. 133Figure 7 7 DSB yield. The DSB yield is the product of secondary electron and DSB cross-section. 134 List of TablesTable 4 1 The calculated LET using mean energy of secondary particles generated by 200 MeV proton irr

adiate on silicon 68Table 5 1 Evaluation index (EI) for LET in layer structure without SiGe. 88Table 5 2 EI for secondary particle yields in layer structure without SiGe. 90Table 5 3 EI for LET in layer structure with SiGe. 94Table 5 4 EI for secondary particle yields in layer structure

with SiGe. 95Table 5 5 Energy deposition analysis results for the layer structure without SiGe for 1010 neutron/proton incident 97Table 5 6 Energy deposition analysis results for the layer structure with SiGe for 1010 neutron/proton incident 98Table 7 1 Frequency mean lineal energy, dose me

an lineal energy and calculated RBE for each irradiation condition 127Table 7 2 Relative dose in cavity and wall for each irradiation condition in same fluence 128Table 7 3 Relative number of secondary electrons generated by unit dose and overall RBE 129 

類鐸受體訊息傳遞路徑參與調控瓢體蟲前端再生之研究

為了解決Fission SV的問題，作者陳巧坪 這樣論述：

受傷時為了避免嚴重感染，免疫系統及發炎反應扮演相當重要的角色，現今的研究者主張免疫系統的發展和再生能力的減弱是互為權衡下的結果，然而，關於受傷所引發的發炎反應是否真的與再生能力有相互調控之關係的研究至今依然相當有限，本研究旨在闡明並釐清類鐸受體訊息傳遞路徑(TLRs signailing pathway)如何參與並調控具有全身再生能力之淡水生環節動物瓢體蟲Aeolosoma viride前端再生，首先我詳細描述瓢體蟲前端及尾端再生的形態學變化，如芽體(blastema)、嘴以及尾板(pygidium)的形成，接著亦證實此過程是透過變形再生(epimorphosis)包含大規模的細胞增生以及極

少數的細胞遷移所完成。另一方面為了使用瓢體蟲為模式研究類鐸受體訊息傳遞路徑參與再生的過程，此路徑中古老且保守的TLR、MyD88以及TNF同源基因亦從瓢體蟲中被鑑定及分析，並分別命名為Avi-TLR-a、Avi-TLR-b、Avi-MyD88-a、Avi-MyD88-i、Avi-TNF-1及Avi-TNF-2，這些基因於瓢體蟲切除頭部後大多先降低其表現量而後才又回升至未受損傷時的水平，其中只有缺少標準MyD88蛋白質序列中的TIR domain，Avi-MyD88-i，於傷口形成後立即大量提升其基因表現量並維持直至再生完成。此外使用抗生素及病原相關分子模式(PAMP)中的聚肌胞苷酸(poly

I:C)進行再生實驗，亦證實可透過促進或抑制細胞增生達到調控再生成功與否的結果，使用聚肌胞苷酸可於再生過程中進行前發炎細胞激素的雙向調控，其一為透過Avi-TLR-a、Avi-MyD88-a及Avi-TNF-1的增加，其二為Avi-TLR-b、Avi-MyD88-i及Avi-TNF-2的基因表現量減少進而抑制了瓢體蟲的再生能力，再者，此雙向調控可被類鐸受體訊息傳遞路徑之抑制劑C34所恢復，利用RNA干擾減少Avi-MyD88-i表現後亦導致瓢體蟲減緩其再生速度，這些結果指出透過類鐸受體訊息傳遞路徑引發的發炎反應於再生過程中需要被正確且確實的調控，而本研究也支持了互為權衡下的免疫系統及再生能力之

理論。