g Simulation 70 

The physics behind microelectronics processes is nowadays sufficiently well known and documented to be accurately simulated in a number of situations. Out of these, ion implantation and diffusion processes in crystalline matter (mainly silicon) are particular fields of focus (see [59] and references within). Various models were developed and are available, explaining the physical mechanisms behind the process itself and verified with experimental data. These mathematical models are often used for design of devices in an optimal way with respect to several criteria and in identifying relevant material properties (see for example [60- 62]). The quantity to be identified or optimised is the dopant depth distribution, the junction depth, the damage inflicted to the substrate during ion implantation processes; and the amount of diffused atoms along with characteristic diffusion parameters. Also of interest is the profile shape before and after annealing. The former can be obtained using Monte Carlo simulation models such as TRIM or Crystal-TRIM [63]. An example of depth profile obtained with Crystal TRIM is displayed in Figure II.18.A. Simulation of diffusion is more complex, since it is strongly dependent on element-, matrix- and environmental specific behaviours such as for instance pairing mechanism (with either vacancies or interstitials), solid solubility in various matrixes and ambient atmosphere (oxidising, wet or dry…). However the physical process itself can always be assimilated to a Fickian mechanism with Arrhenius-like diffusion coefficients [59] including these elemental-, matrix- and environmental-dependent specificities. For example let us consider intrinsic or doped SiGe heterostructures with complex geometries and strain configurations in the nanometric or deca-nanometric scale. The spatial (one dimensional) distribution of both alloy composition and dopants depend on Si, Ge and dopant diffusion upon thermal budget, which are, in turn, determined by the diffusion of point-defects (vacancies and interstitials). The phenomenology of these diffusion processes in SiGe structures can be very complex. In SiGe alloys the lattice parameter is a function of the Ge composition, which implies strain and therefore coupling of strain and compositional effects on diffusion. The diffusion behaviour of such structures upon annealing can be simulated with complete software such as S-Process [64] allowing the user to implement his own parameters to the classical diffusion equations. Ge profiles in SiGe:C/Si superlattices after annealing obtained through use of S-Process are presented in Figure II.18.B.

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Solutions explored to answer ToF-SIMS characterisation needs

Figure II.18.AC-TRIM and ToF-SIMS profile of a 1 keV, 5×1014 at/cm2 As implant in (110) Si with a tilt of 7 degrees relative to sample surface normal. ToF-SIMS profile was obtained with 250 eV Cs+ for sputtering and 25 keV Bi+ for analysis.

BGe concentration profiles of a four-period SiGe:C/Si superlattice sample annealed for 2 minutes in inert atmosphere at 850, 950 and 1050C obtained with S-Process.

Various models can be applied to the simulation of semiconductor processes. They yield an extremely rich panel of information out of which only the extraction of physical parameters is of use in our study. Furthermore, although in actual nanometric devices three dimensional effects are observed, in full sheet structures in which those can be negligible. We are thus interested in the acquisition of the following, one dimensional information:

- Junction depth and dopant profile (quantitative) - Major elements distribution after annealing (quantitative)

- In-depth stress distribution (quantitative)

This information can be used in correlation with ToF-SIMS (or any other technique) depth profiles to cross-characterise a sample. This will help assess the accuracy of ToF-SIMS analysis. It will also bring physical comprehension of some of the phenomena observed in ToF-SIMS profiles, such as chemically- or strain-enhanced diffusion behaviours.

CHAPTER II Conclusion

In this chapter we comprehensively reviewed all the solutions used in this thesis work to enhance the quality of a ToF-SIMS analysis. These are either specific to a particular kind of sample or can be applied to any kind of material and structures. Particularly data treatment and comparison with other techniques will be widely used in the following applicative chapters. Although for clarity each option was treated separately in this chapter, in reality they were used together in order to meet the characterization needs. In the next chapters we will therefore describe the practical setup of those measures in the different materials and structures presented in chapter I, the improvements they could bring and the interpretation of the improved ToF-SIMS measurements in terms of material science and device process understanding.

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Solutions explored to answer ToF-SIMS characterisation needs

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Solutions explored to answer ToF-SIMS characterisation needs

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