Profile evolution

Hwang G S, Anderson C M, Gordon M J, Moore T A, Minton T K and Glapis K P 1996 Gas-surface dynamics and profile evolution during etching of silicon Phys. Rev. Lett. 77 3049-51... 

P. M. Duxbury, W. Selke. Surface profile evolution above roughening. Z Physik B 04 311-318, 1994 P. M. Duxbury, W. Selke. EquiUbration of crystal surfaces. Phys Rev B 52 17468-17479, 1995. 

Fig. 30. Simulation of profile evolution in a dual-material (selective polish) system [8]. Reproduced by permission of the Electrochemical Society, Inc.

Figure A3-3-4 Diffusion profile evolution in a "spherical diffusion couple."...

Figure 3. Density distribution required to fit the observed line profile evolution. The density falls as v-11, and the integrated mass above 6000 km s 1 is about 2 solar masses.

Fig. 9. (a) Groove depth evolution curves, (b) Groove width evolution curves. When free evaporation and surface diffusion are considered simultaneously, the groove profile evolution increases more slowly and tends asymptotically to a constant value (108). 

Stonestrom D. A., White A. F., and Akstin K. C. (1998) Determining rates of chemical weathering in soils-solute transport versus profile evolution. J. Hydrol. 209, 331-345. 

For the start up flow case, we obtain the following set of curves of the dimensionless velocity profiles evolution shown in Figure 1, where the increase in the wall velocity with time can be clearly observed. The three dimensional plot for the velocity distribution is given in Figure 2 for the periodic case, and we can observe the quasi-steady-state (periodic state) establishment, and the time variation of the dimensionless slip velocity. 

Figure 5.10. Profile evolution of ionic Cu concentration in the Gerlache Inlet during the 1990-1991 campaign. Bl, November 24 B2, November 29 B4, December 7 B6, December 26 B7, January 6 B8, January 30 B9, February II. Reprinted from Capodaglio et al. (134), with permission of Gordon and Breach Publishers, Reading, UK.

Figure 3.6 Schematic representation of the downward advancement of the weathering front, showing the relative changes in abundances (shaded) of the major lateritic components, Si and Fe, during profile evolution.

The structural evolution of an a-Si H film deposited by MD at 773 K with SiHg as the sole deposition precursor is shown in Fig. 10 (Ramalingam, 2000). Figure 10a shows the profile evolution in the growth direction, z, of... 

Fig. 10. Structural evolution of an a -Si 11 film during its deposition by MD simulation (from Ramalingam, 2000). (a) Profile evolution ot the order parameter, f, in the direction of growth, profiles are shown after 1,2. and 5 ns. respectively, (b) Si-Si radial distribution function. g(r), after 2 and 5 ns. (c) a-Si H/c-Si film/substrate conhguration after 5 ns of film growth simulation.

The effect of aspect ratio on superfilling and shape of the deposited copper is shown in Figure 3. On the right, the profile evolution in a 0.2 fim trench with aspect ratio of 2 (i.e. the insulator thickness is 0.4 fim) is shown. Figure 3 on the left shows the deposited copper profile in a 0.2 fim trench but with aspect ratio of 5. The parameters used for these simulations are b=17.8 and p=0.25. Both trenches fill well without voids or seams. However, the line with AR of 5 fills up more abruptly than the line with AR of 2 which fills up more sequentially. There is always a timestep in the high aspect ratio trench after which, the line fills from the bottom up. Also, as expected, the shape evolving in the AR of 2 line is more rounded than the shape of copper deposited in the AR of 5 line. 

In this paper we describe a model of a cup plater with a peripheral continuous contact and passive elements that shape the potential field. The model takes into account the ohmic drop in the electrolyte, the charge-transfer overpotential at the electrode surface, the ohmic drop within the seed layer, and the transient effect of the growing metal film as it plates up (treated as a series of pseudo-steady time steps). Comparison of experimental plated thickness profiles with thickness profile evolution predicted by the model is shown. Tool scale-up for 300 mm wafers was also simulated and compared with results from a dimensionless analysis. 

The shape evolution of microfeatures is followed using a number of techniques. The string model was popular at first [191], but profile advancement based on the method of characteristics [192, 193] is more robust. In another method [194, 195] the solid is divided into a large number of elements (digitized). The volume digits are removed (etching) based on the local etch rate and a local mass balance. If deposition takes place a volume element is added locally. Unfortunately, items (b)-(e) above are largely unknown for almost all material systems. Surface ehemistry in particular is one of the major limitations of current models of profile evolution. One is then forced to use a number of adjustable parameters that limit the predictive value of the model. 

In the determination of the effective density, p(x, y, z), the effect of laf-eral deposition is accounted for by adding a bias term to the metal lines, which constitute the mask layout pattern. This ensures that the effective density is that of fhe final film profile and not the initial mask layout. It is assumed that the local pattern density is independent of the film thickness before the local planarity approximates the actual deposition profiles wifh a vertical profile. In realify, fhe effective density of the exposed surface depends on fhe heighf if is possible to "time step" the profile evolution to accounf for such a time-varying densify, buf such detail is not essential for the prediction of final oxide fhickness. The assumption makes it possible to express the final film fhickness for any time, f, in a closed form as ... 

By combining clustering with a machine learning process, we could be able to predict the development of physical autonomy loss or mental autonomy loss in elderly people over time. To reach this objective, we use machine learning approach based on grammar inference in order to infer a probabilistic automaton. In the article, we only present the patients profiles evolution regarding to upper body functional disorders (cognitive impairment). 

Profile evolution techniques measitre only diffusion driven by chemical gradients (as opposed to tracer diffusion) for heterodiffirsion, but in suitable profile geometries can do so very directly with a minimitm of complicated modeling. The utility of a given method is hmited by (a) the variety of adsorbates it can monitor without major siuface perturbation, (b) the spatial resolution it can attain (including initial profile formation), and (c) the suitability of the initial profile geometry for qrrantitative analysis. 

Rash Profile evolution and uses in sensory and consumer science... 

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