Device simulation

F. Dessenne, D. Cichocka, P. Desplanques, R. Fauquembergue. Comparison of wurtzite and zinc blende III-V nitrides field effect transistors a 2D Monte Carlo device simulation. Mater Sci Eng B 50 315, 1997. 

Medici Two-Dimensional Device Simulation Program Version 2002, User s Manual, Avanti Corporation, TCAD Businenti Corporation, TCAD Business Unit, Fremont, CA, July 2000. 

It is provided with manual safety and/or setting devices simulating those of a standard or proposed standard PDF. It may or may not be a ballistic match with the fuze it is supposed to represent and/or contains a small expl chge for realism or spotting purposes (Excludes Fuze, Point Detonating, Dummy)... 

Costamagna, P., Arato, E., Achenbach, E. and Reus, U. (1994) Fluid dynamic study of fuel cell devices simulation and experimental validation, Journal of Power Sources 52, 243-249. 

Detcheverry, C. and Matters, M. (2000) Device simulation of all-polymer thin-film transistors. Proc. ESSDERC, 328-331. 

Jacoboni, C., and Luigi, P. (1989). The Monte Carlo Method for Semiconductor Device Simulation. New York Springer Verlag. 

Gummel, H.K. (1964) A Self-consistent Iterative Scheme for One-dimensional Steady State Transistor Calculations. IEEE Trans. Electron Devices, ED-11, 455-465. Lee, C.M., Lomax, R.J. and Haddad, G.I. (1974) Semiconductor Device Simulation. IEEE Trans. Microw. Theory Techn., MTT-22, 160-177. 

Grasser, T., Tang, T.W., Kosina, H. and Selberherr, S. (2003) A Review of Hydrodynamic and Energy-Transport Models for Semiconductor Device Simulation. Proc. IEEE, 91, 251-274. 

Close to the drain contact, the simulated potential profile for Uq = -30 V is in good agreement with the observed potentiometry trace shown in Figure 20.2. Based on previous potentiometry measurements on polymer OFETs revealing a substantial potential drop at the drain contact [13], two-dimensional device simulations have suggested that the mobility close to the contacts... 

Figure 20.5 (a) Electrostatic potential and (b) hole density obtained in the two-dimensional device simulation for = -30 V, within the first 0.4 pm from the source contact. 

DBST (p,p -dibutylsexithiophene) 77, 80 ff DCNDBQT (a,cD-dicyano-p,p -dibutyl-quaterthiophene) 76 ff deep level transient spectroscopy (DLTS) 428, 437, 438 deep trap 437, 433, 441 deformation pattern 264, 276 degradation 373 ff., 393 ff, 553 demodulated reader signal 9 density functional theory (DFT) 264, 539 density of states (DOS) 428, 437 depth profile 404 ff., 436, 544 de-trapping 428, 437 ff, 441 device simulation 433, 435 dewetting, post-deposition 220 ff. DHBTP-SC ((dihexylbithiophene)2-phe-nyl swivel cruciform) 96 ff. 

As is evident from equation 4, Nip can be made zero by making both (<4 — and (rrxx — ffyy) zero. Fig. 7.3.19 shows the result of FEM stress analysis by sensor device simulation. With the piezoresistive gages arranged concentrically with the inner circumference of the metal stem, both n xx — other hand, since TNS is proportional to the temperature characteristics of (o, /x — [Pg.328]

The silicon photodiode output is analyzed using a device simulator intended for analyzing the semiconductor device electrical characteristics, with the added capability of analyzing the movement of electrons/holes when the silicon photodiode is exposed to light. Fig. 7.13.4, Fig. 7.13.5, and Fig. 7.13.6 show a photodiode analysis model, a mesh diagram, and an impurity density distribution map. 

Governing Equations. If the problem is to be solved rigorously, the BTE must be solved for electrons in each valley, optical phonons, and acoustic phonons. The distribution function of each of these depends on six variables—three space and three momentum (or energy). The solution to BTE for this complexity becomes very computer intensive, especially due to the fact that the timescales of electron-phonon and phonon-phonon interactions vary by two orders of magnitude. Monte Carlo simulations are sometimes used although this, too, is very time-consuming. Therefore, researchers have resorted mainly to hydrodynamic equations for modeling electron and phonon transport for practical device simulation. 

Recent topics in SiC research in Japan are micropipes in wafers, control of polytype in epitaxy, surface analysis of epitaxial layers, application for power devices, device simulation, and others [10]. 

The electronic devices simulate the different stages of the human olfactory system, resulting in volatile odor recognition, which can now be used to discriminate between different bacterial infections. (Turner Magan, 2004)... 

Focusing of ions in curved FAIMS (4.3.1) means a pseudopotential bottoming near the gap median. Devices using such wells to guide or trap ions (e.g., quadrupole filters or traps and electrodynamic funnels) have finite charge capacity or saturation current (/sat) the Coulomb potential scales as the charge density squared and, above some density, exceeds the well depth and expels excess ions from the device. Simulations... 

Automatic macromodel extraction that takes advantage of high-fidelity device simulations (e.g., FEM, FVM, and BEM) to extract RLC values in irregular microchannel geometries has also been reported. Turowski et al. [11] approximate the microfluidic Tesla valve as an/ L circuit (serial connection of a resistor R and an inductor L in Fig. 5) and performed both steady and transient analysis to extract its fluidic resistance and inductance. The macromodels are then stitched together for an overall system simulation on the pumping performance. 

An advantage of simulation is lack of device, simulated by vendor software, what is cheaper way of checking its proper operation into a network, than buying a device. A disadvantage of described method is delimitation of software s functionality only to specific vendor. There is no possibility to check, in virtual way, how network devices made by different manufactures would be cooperate. 

From these data it can be seen that about half of all of the adherent particles (yp = 46-61%) were removed under the influence of wind, or, more precisely, were removed by the use of a device simulating wind. The rainfall removed more particles than did the wind. The effectiveness of particle removal was dependent on both the nature of the powder and the properties of the surface. 

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