Future challenges in CMOS process modeling

Pichler, P.; Burenkov, A.; Lorenz, J.; Kampen, C.; Frey, L.

doi:10.1016/j.tsf.2009.09.150

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…Such complex techniques like stress memorization 40 are difficult to modelize even empirically and are interesting challenges for future research work. 41 …”

Section: Discussionmentioning

confidence: 99%

Stress mapping in strain-engineered silicon p-type MOSFET device: A comparison between process simulation and experiments

Krzeminski

2012

Journal of Vacuum Science &Amp; Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phen

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8 pagesInternational audienceStrain engineering is the main technological booster used by semiconductor companies for the 65 and 45nm technology nodes to improve the channel mobility and the electrical performance of logic devices. For 32 and 22nm nodes, intense research work focuses on the integration and optimisation of these different techniques by cumulating the effects of different stressors. To estimate the level and the distribution of the stress field generated in the transistor channel by such multiple processing steps is a complex issue. Process simulation has a role to play in order to face the many challenges in term of scalability, yield and design. The objective of this paper is first to evaluate the stress distribution generated by the two most usual processing steps: Contact Etch Stop Liner (CESL) and embedded SiGe Stressors (eSiGe). Next, the final stress field in nanoscale device resulting of these intentionally but also parasitic sources are evaluated. Process simulations have been able to quantify the global trend observed in these stressors in relatively close correlation with experiment

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“…Such complex techniques like stress memorization 40 are difficult to modelize even empirically and are interesting challenges for future research work. 41 …”

Section: Discussionmentioning

confidence: 99%

Stress mapping in strain-engineered silicon p-type MOSFET device: A comparison between process simulation and experiments

Krzeminski

2012

Journal of Vacuum Science &Amp; Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phen

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“…Despite that analytical solutions provide exact results over a short time scale, their use is generally limited to experiments of reduced complexity (at thermodynamic equilibrium, with constant diffusion coefficients…). For example, the measurement of the diffusion coefficient of impurities in monocrystalline Si layers is often performed via numerical simulations, due to the complexity of the diffusion mechanisms (simultaneous use of interstitial and vacancy mediated mechanisms using point defects of various charge states, diffusion coefficient dependence of impurity concentration, transient diffusion out of equilibrium…) [3][4][5]. Furthermore, one-dimensional (1D) numerical simulations can be used to measure diffusion coefficients, and once these coefficients are known, two-dimensional (2D) simulations can be used to simulate atom diffusion in complex structures as in transistors during fabrication processes [6].…”

Section: Introductionmentioning

confidence: 99%

Original Methods for Diffusion Measurements in Polycrystalline Thin Films

Portavoce

Blum

Hoummada

et al. 2012

DDF

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With the development of nanotechnologies, the number of industrial processes dealing with the production of nanostructures or nanoobjects is in constant progress (microelectronics, metallurgy). Thus, knowledge of atom mobility and the understanding of atom redistribution in nanoobjects and during their fabrication have become subjects of increasing importance, since they are key parameters to control nanofabrication. Especially, todays materials can be both composed of nanoobjects as clusters or decorated defects, and contain a large number of interfaces as in nanometer-thick film stacking and buried nanowires or nanoislands. Atom redistribution in this type of materials is quite complex due to the combination of different effects, such as composition and stress, and is still not very well known due to experimental issues. For example, it has been shown that atomic transport in nanocrystalline layers can be several orders of magnitude faster than in microcrystalline layers, though the reason for this mobility increase is still under debate. Effective diffusion in nanocrystalline layers is expected to be highly dependent on interface and grain boundary (GB) diffusion, as well as triple junction diffusion. However, experimental measurements of diffusion coefficients in nanograins, nanograin boundaries, triple junctions, and interfaces, as well as investigations concerning diffusion mechanisms, and defect formation and mobility in these different diffusion paths are today still needed, in order to give a complete picture of nanodiffusion and nanosize effects upon atom transport. In this paper, we present recent studies dealing with diffusion in nanocrystalline materials using original simulations combined with usual 1D composition profile measurements, or using the particular abilities of atom probe tomography (APT) to experimentally characterize interfaces. We present techniques allowing for the simultaneous measurement of grain and GB diffusion coefficients in polycrystals, as well as the measurement of nanograin lattice diffusion and triple junction diffusion. We also show that laser-assisted APT microscopy is the ideal tool to study interface diffusion and nanodiffusion in nanostructures, since it allows the determination of 1D, 2D and 3D atomic distributions that can be analyzed using diffusion analytical solutions or numerical simulation.

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“…It is important to note that to be used for industry applications, simulations have to fulfill several requirements: i) they should reproduce experimental condition variations during the process (stress, temperature, atmosphere…), ii) at the experimental scale (size −m, cm, µm, nm, and time −seconds, minutes, hours), iii) the results must be quantitative, iv) they should be obtained in a minimum time scale, and v) the simulations' support (software) should be as friendly as possible to use, allowing frequent modifications performed by different users. Impurity diffusion coefficient measurements, as well as diffusion simulations in mono-crystalline Si (mono-Si) are widely performed in one-and two-dimensions using FES [4]. However, despite intensive FES of mass transport and of grain boundary motion in poly-crystals [5][6][7][8][9][10], diffusion coefficients in poly-crystals are mainly measured in strict kinetic regime conditions (A, B or C) using analytical solutions (Fisher's model for the regime B, for example) [1].…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation Support for Diffusion Coefficient Measurements in Polycrystalline Thin Films

Portavoce

Blum

Chow

et al. 2011

DDF

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The measurement of diffusion coefficients in today’s materials is complicated by the down scaling of the studied structures (nanometric effects in thin films, nano-crystalline layers, etc.) and by the complex production process conditions of industrial samples or structures (temperature variations, complex solute and point defect distributions, stress gradients, etc.). Often diffusion measurements have to be performed in samples for which initial experimental conditions do not offer the possibility of using conventional diffusion analytical solutions. Furthermore, phenomena involved with diffusion are sometimes so numerous and complex (stress, matrix composition inhomogeneities, time dependence of point defect generation sources, electrical effects, clustering effects, etc…) that the use of analytical solutions to solve the observed diffusion behavior is difficult. However, simulations can be of use in these cases. They are time consuming compared to the use of analytical solutions, but are more flexible regarding initial conditions and problem complexity. The use of simulations in order to model physical phenomena is quite common nowadays, and highly complex models have been developed. However, two types of simulations have to be considered: i) simulations aiming to understand and predict phenomena, and ii) simulations for measurement purposes, aiming to extract the (average) value of a physical parameter from experimental data. These two cases have different constrains. In the second case, that is the subject of this article, one of the most important stress is that the simulation has to precisely scale the experiment (sample size, experiment duration, etc.), sometimes preventing the measurement due to computational time consumption. Furthermore, the simpler the model (small number of parameters) used in the simulation, the more relevant the measurement (minimum error). In this paper, examples of recent works using two- and three-dimensional finite element simulations for diffusion coefficient measurements in thin polycrystalline films and nano-crystalline layers are presented. The possible use of simulations for diffusion coefficient measurements considering GB migration, GB segregation, or triple junctions is also discussed.

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Future challenges in CMOS process modeling

Cited by 6 publications

References 29 publications

Stress mapping in strain-engineered silicon p-type MOSFET device: A comparison between process simulation and experiments

Stress mapping in strain-engineered silicon p-type MOSFET device: A comparison between process simulation and experiments

Original Methods for Diffusion Measurements in Polycrystalline Thin Films

Numerical Simulation Support for Diffusion Coefficient Measurements in Polycrystalline Thin Films

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