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Online since: June 2012
Authors: Zheng Cheng Zhang, Hai Zhong Liu
Some aging properties and stochastic comparisons on the residual life are obtained.
1.
For more details about order statistics, see [1], and [2] etc.
And from Theorem 1.C.37 of [4], for all , and , hence from Theorem 1.C.17 of [4] again, the result holds.
References [1] H.A.
Wang, The NBUC and NWUC classes of life distributions, Journal of Applied Probability 31 (1991) 473-479
For more details about order statistics, see [1], and [2] etc.
And from Theorem 1.C.37 of [4], for all , and , hence from Theorem 1.C.17 of [4] again, the result holds.
References [1] H.A.
Wang, The NBUC and NWUC classes of life distributions, Journal of Applied Probability 31 (1991) 473-479
Online since: December 2012
Authors: Sophia Arnauts, Dennis H. van Dorp, Daniel Cuypers, Paul W. Mertens, Stefan de Gendt
Cleaning of III-V Materials: Surface Chemistry Considerations
Dennis H. van Dorp1, a, Daniel Cuypers2, Sophia Arnauts1, Paul Mertens1,
and Stefan De Gendt1, 2
1 IMEC Interuniversity Microelectronics Center, Kapeldreef 75, B-3001 Leuven, Belgium
2 Katholieke Universiteit Leuven, Celestijnenlaan 200F B-3001, Leuven, Belgium
avandorpd@imec.be
Keywords: III-V cleaning, indium phosphide, etch rates, oxide removal
Introduction
Compound semiconductors based on group III and V elements of the periodic system have high charge carrier mobility and are, therefore, candidates for channel material in future CMOS devices [1].
In this model H2O2 is used to oxidize the surface to form an oxide: InP + 2H2O2 ® InPO4 + 2H2 (1) which is in equilibrium with dissolution in acid: InPO4 + 3HCl ® PO43- + InCl3 + 3H+ (2) It is clear that this model only works when the rate of reaction (1) is higher than that of (2).
In Figure 1(a) the influence of the H2O2 concentration on the etch rate in 1M HCl is shown.
b a Figure 1: (a) Etch rate for InP for various H2O2 concentrations in 1M HCl (squares) and 1M H2SO4 (circles).
References [1] J.A. del Alamo: Nature 479 (2011), p. 317 [2] P.
In this model H2O2 is used to oxidize the surface to form an oxide: InP + 2H2O2 ® InPO4 + 2H2 (1) which is in equilibrium with dissolution in acid: InPO4 + 3HCl ® PO43- + InCl3 + 3H+ (2) It is clear that this model only works when the rate of reaction (1) is higher than that of (2).
In Figure 1(a) the influence of the H2O2 concentration on the etch rate in 1M HCl is shown.
b a Figure 1: (a) Etch rate for InP for various H2O2 concentrations in 1M HCl (squares) and 1M H2SO4 (circles).
References [1] J.A. del Alamo: Nature 479 (2011), p. 317 [2] P.
Online since: January 2013
Authors: Zhi Zhi Hu, Yun Hua Lu, Bing Wang, Peng Pan, Hong Bin Zhao
Fig. 3 and Table 1 show the optical properties of PI-1 and PI-2 films.
Fig.3 UV-vis spectrum of PI films Fig.4 TGA curves of PIs Table 1 The properties of PI films PI λcutoffa [nm] T410nma [%] T5%b [˚C] T10%b [˚C] Rw750b [%] PI-1 305.9 81.9 412 471 47.33 PI-2 301.2 83.2 423 479 48.68 aλcutoff: UV cutoff wavelength; T410nm: transmittance at 410 nm. bT5%, T10%: temperatures at 5% and 10% weight loss, respectively; Rw750: residual weight ratio at 750 oC in nitrogen.
The 10% decomposition temperatures of PI-1 and PI-2 were 471 and 479˚C respectively.
Conclusions The fluorinated PI-1 and PI-2 were prepared with 2-tert-butyl-1,4-bis(4-nitro-2-trifluoromethyl phenoxy)benzene, 1,4-bis(4-amino-2-trifluoromethylphenoxy)benzene and 1,2,3,4- cyclobutanetetracarboxylic dianhydride, respectively.
References [1] C.
Fig.3 UV-vis spectrum of PI films Fig.4 TGA curves of PIs Table 1 The properties of PI films PI λcutoffa [nm] T410nma [%] T5%b [˚C] T10%b [˚C] Rw750b [%] PI-1 305.9 81.9 412 471 47.33 PI-2 301.2 83.2 423 479 48.68 aλcutoff: UV cutoff wavelength; T410nm: transmittance at 410 nm. bT5%, T10%: temperatures at 5% and 10% weight loss, respectively; Rw750: residual weight ratio at 750 oC in nitrogen.
The 10% decomposition temperatures of PI-1 and PI-2 were 471 and 479˚C respectively.
Conclusions The fluorinated PI-1 and PI-2 were prepared with 2-tert-butyl-1,4-bis(4-nitro-2-trifluoromethyl phenoxy)benzene, 1,4-bis(4-amino-2-trifluoromethylphenoxy)benzene and 1,2,3,4- cyclobutanetetracarboxylic dianhydride, respectively.
References [1] C.
Online since: June 2015
Authors: Mohd Sobri Idris, T.Q. Tan, Rozana Aina Maulat Osman
The lattice parameters for the indexed pattern for the sample that heated at 900 ºC are a= 2.8634(1) Å and c = 14.248(1) Å while for sample that heated at 950 ºC are a = 2.8639(1) Å and c = 14.253(1) Å.
The final model for each temperature is summarized in Table 1.
Temperature (ºC) 900 950 a / Å 2.8634(1) 2.8639(1) c / Å 14.248(1) 14.253(1) Volume / Å3 101.16(1) 101.24(1) Oxygen, Z 0.2428(2) 0.2427(2) 3a Li/Ni occ. 0.973(2) / 0.027(2) 0.979(2) / 0.021(2) 3b Ni/Li occ. 0.306(2) / 0.027(2) 0.312(2) / 0.021(2) 6c O occ. 1.00 1.00 3a Uiso 0.02 0.02 3b Uiso 0.006 0.006 6c Uiso 0.003 0.003 Rwp 3.45 % 3.99 % Rp 2.61 % 2.88 % χ2 0.7457 0.7292 (a) (b) Fig. 2: Rietveld plot of the LiNi1/3Mn1/3Co1/3O2 synthesised at (a) 900 and (b) 950 ºC in oxygen for 12 hours.
References [1] Y.
Osman, Structure refinement strategy of Li-based complex oxides using GSAS-EXPGUI software package, Advanced Materials Research 795 (2013) 479-482
The final model for each temperature is summarized in Table 1.
Temperature (ºC) 900 950 a / Å 2.8634(1) 2.8639(1) c / Å 14.248(1) 14.253(1) Volume / Å3 101.16(1) 101.24(1) Oxygen, Z 0.2428(2) 0.2427(2) 3a Li/Ni occ. 0.973(2) / 0.027(2) 0.979(2) / 0.021(2) 3b Ni/Li occ. 0.306(2) / 0.027(2) 0.312(2) / 0.021(2) 6c O occ. 1.00 1.00 3a Uiso 0.02 0.02 3b Uiso 0.006 0.006 6c Uiso 0.003 0.003 Rwp 3.45 % 3.99 % Rp 2.61 % 2.88 % χ2 0.7457 0.7292 (a) (b) Fig. 2: Rietveld plot of the LiNi1/3Mn1/3Co1/3O2 synthesised at (a) 900 and (b) 950 ºC in oxygen for 12 hours.
References [1] Y.
Osman, Structure refinement strategy of Li-based complex oxides using GSAS-EXPGUI software package, Advanced Materials Research 795 (2013) 479-482
Online since: February 2006
Authors: Masayuki Nogami, Kazuhiko Hattori, N. Saito, S. Okuda, M. Oida, N. Isu
Oida
1, N.
Isu 1, S.
Okuda 1, N.
Nogami3,b 1 General Research Institute of Technology, INAX Corporation MInatomachi, Tokoname 479-8588, Japan 2 Development Office, Tile & Building Materials Division, INAX Corporation Kume, Tokoname 479-0002, Japan 3 Department of Materials Science and Engineering, Graduate School of Engineering Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan a kazu@i2.inax.co.jp, bnogami@nitech.ac.jp Keywords: Solidifing, Urban heat islands, Inorganic waste, Paving tile, Numerical simulation Abstract.
Table 1.
Isu 1, S.
Okuda 1, N.
Nogami3,b 1 General Research Institute of Technology, INAX Corporation MInatomachi, Tokoname 479-8588, Japan 2 Development Office, Tile & Building Materials Division, INAX Corporation Kume, Tokoname 479-0002, Japan 3 Department of Materials Science and Engineering, Graduate School of Engineering Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan a kazu@i2.inax.co.jp, bnogami@nitech.ac.jp Keywords: Solidifing, Urban heat islands, Inorganic waste, Paving tile, Numerical simulation Abstract.
Table 1.
Online since: December 2010
Authors: Jian Zhang, Qiang Hua Li
System principle, buildup and explanation
The correlated demodulation principle based on the edge of FBG optical band-pass filter Matched FBG dynamic sensing and demodulation system is shown in Fig.1.
a) Sensing and Demodulation system b) Demodulation thoery schematic Figure 1.
Reference [1] AKIRAM, ISAMU Y.
SP IE, 2001, 4330 (479), 479~ 486 [2] Fan Dian, Jiang De-sheng, Mei Jian-cun.
Acta Photonic Sonica, 2006, 35(1), 118~121 [3] Li Dong-sheng, Sui Qing-mei, Cao Yu-qiang.
a) Sensing and Demodulation system b) Demodulation thoery schematic Figure 1.
Reference [1] AKIRAM, ISAMU Y.
SP IE, 2001, 4330 (479), 479~ 486 [2] Fan Dian, Jiang De-sheng, Mei Jian-cun.
Acta Photonic Sonica, 2006, 35(1), 118~121 [3] Li Dong-sheng, Sui Qing-mei, Cao Yu-qiang.
Online since: November 2012
Authors: Wei Liang, Li Na Zhang, Xiao Wei Li, Yan Di Zuo
The data processing is showed in Figure 1.The output variables of sensor array signal are reduced by principal component analysis, and then get the low-dimensional variables.
We can see that the correlation coefficient between the G1 and G2, G3 and G4, G5 and G7,G6 and G8 are all above 0.9 from the Correlation Matrix shown in Table 1 .
Table 1 Correlation Matrix r Correlation G1 G2 G3 G4 G5 G6 G7 G8 G1 G2 G3 G4 G5 G6 G7 G8 1.000 .997 -.303 -.263 -.259 .633 -.297 .601 .997 1.000 -.346 -.310 -.195 .688 -.237 .645 -.303 -.346 1.000 .992 -.190 -.514 -.081 -.172 -.263 -.310 .992 1.000 -.240 -.522 -.133 -.185 -.259 -.195 -.190 -.240 1.000 .487 .994 .479 .633 .688 -.514 -.522 .487 1.000 .436 .933 -.297 -.237 -.081 -.133 .994 .436 1.000 .467 .601 .645 -.172 -.185 .479 .933 .467 1.000 Generally, the correlation between the principle component and sensors are most same.
The mapping of normalization was as follows: (x, y∈,=min(x), = max(x) ) The normalized raw data is structured to [0, 1].
References [1] Cheng SM.
We can see that the correlation coefficient between the G1 and G2, G3 and G4, G5 and G7,G6 and G8 are all above 0.9 from the Correlation Matrix shown in Table 1 .
Table 1 Correlation Matrix r Correlation G1 G2 G3 G4 G5 G6 G7 G8 G1 G2 G3 G4 G5 G6 G7 G8 1.000 .997 -.303 -.263 -.259 .633 -.297 .601 .997 1.000 -.346 -.310 -.195 .688 -.237 .645 -.303 -.346 1.000 .992 -.190 -.514 -.081 -.172 -.263 -.310 .992 1.000 -.240 -.522 -.133 -.185 -.259 -.195 -.190 -.240 1.000 .487 .994 .479 .633 .688 -.514 -.522 .487 1.000 .436 .933 -.297 -.237 -.081 -.133 .994 .436 1.000 .467 .601 .645 -.172 -.185 .479 .933 .467 1.000 Generally, the correlation between the principle component and sensors are most same.
The mapping of normalization was as follows: (x, y∈,=min(x), = max(x) ) The normalized raw data is structured to [0, 1].
References [1] Cheng SM.