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$C_{x}F_{y}$ Polymer Film Deposition in rf and dc $C_{7}F_{16}$ Vapor Plasmas
Sakai, Y.,Akazawa, M.,Sakai, Yosuke,Sugawara, H.,Tabata, M.,Lungu, C.P.,Lungu, A.M. The Korean Institute of Electrical and Electronic 2001 Transactions on Electrical and Electronic Material Vol.2 No.1
$C_{x}F_{y}$ polymer film was deposited in rf and dc Fluorinert vapor ($C_{7}F_{16}$) plasmas. In the plasma phase, the spatial distribution of optical emission spectra and the temporal concentration of decomposed species were monitored, and kinetics of the $C_{7}F_{16}$ decomposition process was discussed. Deposition of $C_{x}F_{y}$ film has been tried on substrates of stainless steel, glass, molybdenum and silicon wafers at room temperature in the vapor pressures of 40 and 100 Pa. The films deposited in the rf plasma showed excellent electrical properties as an insulator for multi-layered interconnection of deep-submicron LSI, i.e. the low dielectric constant ∼2.0, the dielectric strength ∼2 MV/cm and the high deposition rate ∼100nm/min at 100W input power.
Complexity Control Method of Chaos Dynamics in Recurrent Neural Networks
Sakai, Masao,Homma, Noriyasu,Abe, Kenichi Institute of Control 2002 Transaction on control, automation and systems eng Vol.4 No.2
This paper demonstrates that the largest Lyapunov exponent λ of recurrent neural networks can be controlled efficiently by a stochastic gradient method. An essential core of the proposed method is a novel stochastic approximate formulation of the Lyapunov exponent λ as a function of the network parameters such as connection weights and thresholds of neural activation functions. By a gradient method, a direct calculation to minimize a square error (λ - λ$\^$obj/)$^2$, where λ$\^$obj/ is a desired exponent value, needs gradients collection through time which are given by a recursive calculation from past to present values. The collection is computationally expensive and causes unstable control of the exponent for networks with chaotic dynamics because of chaotic instability. The stochastic formulation derived in this paper gives us an approximation of the gradients collection in a fashion without the recursive calculation. This approximation can realize not only a faster calculation of the gradient, but also stable control for chaotic dynamics. Due to the non-recursive calculation. without respect to the time evolutions, the running times of this approximation grow only about as N$^2$ compared to as N$\^$5/T that is of the direct calculation method. It is also shown by simulation studies that the approximation is a robust formulation for the network size and that proposed method can control the chaos dynamics in recurrent neural networks efficiently.
Establishment of "A-PPNS", A Navigation System for Regenerating the Software Development Business
Sakai, Hirotake,Waji, Yoshihiro,Nakamura, Mari,Amasaka, Kakuro Korean Institute of Industrial Engineers 2011 Industrial Engineeering & Management Systems Vol.10 No.1
Currently, knowledge within the field of software development is largely implicit and is not formally disseminated and shared. This means that there is little improvement and regeneration of processes, and knowledge gained from previous projects is not necessarily applied to new ones. In order to turn this situation around it is necessary to take an organized approach to sharing job-related information. For this study, the authors constructed "Amalab-Project Planning Navigation System, or A-PPNS", a system for organizing accumulated knowledge related to the field of software development. More specifically, A-PPNS is a business process monitoring system and consists of the following four elements: (i) Optimized estimate support subsystem, (ii) Schedule monitoring system, (iii) QCD optimization diagnostic system, and (iv) Strategic technology accumulation system. The effectiveness of this system has been demonstrated and verified at Company A, a system integration company.