Atomistic calculations of ion implantation in Si: Point defect and transient enhanced diffusion phenomena

Jaraı́z, M.; Gilmer, George H.; Poate, J. M.; Rubia, T. Dı́az de la

doi:10.1063/1.116701

Cited by 179 publications

(71 citation statements)

References 4 publications

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“…The simulation results suggest a reduction of 40% of the BCA-estimated total implantation damage. The postimplantation damage is stored in small defect aggregates [26] corresponding to space-dependent cluster size distributions of I -and V -type aggregates. Note that the I and V distributions are always superimposed due to fast rates of the point-defect-pointdefect or point-defect-cluster interactions with respect to the particle diffusion.…”

Section: A Calibration and Initialization Of Kmc And Pde Modelsmentioning

confidence: 99%

Kinetic Monte Carlo simulations for transient thermal fields: Computational methodology and application to the submicrosecond laser processes in implanted silicon

et al. 2012

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Pulsed laser irradiation of damaged solids promotes ultrafast nonequilibrium kinetics, on the submicrosecond scale, leading to microscopic modifications of the material state. Reliable theoretical predictions of this evolution can be achieved only by simulating particle interactions in the presence of large and transient gradients of the thermal field. We propose a kinetic Monte Carlo (KMC) method for the simulation of damaged systems in the extremely far-from-equilibrium conditions caused by the laser irradiation. The reference systems are nonideal crystals containing point defect excesses, an order of magnitude larger than the equilibrium density, due to a preirradiation ion implantation process. The thermal and, eventual, melting problem is solved within the phase-field methodology, and the numerical solutions for the space-and time-dependent thermal field were then dynamically coupled to the KMC code. The formalism, implementation, and related tests of our computational code are discussed in detail.As an application example we analyze the evolution of the defect system caused by P ion implantation in Si under nanosecond pulsed irradiation. The simulation results suggest a significant annihilation of the implantation damage which can be well controlled by the laser fluence.

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Section: A Calibration and Initialization Of Kmc And Pde Modelsmentioning

confidence: 99%

Kinetic Monte Carlo simulations for transient thermal fields: Computational methodology and application to the submicrosecond laser processes in implanted silicon

et al. 2012

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“…Such treatments Will be critical to an eventual filly predictive modeling of the transient enhanced d f i i o n process. [5] higher temperature annealing these clusters provide a source of point defects for subsequent interstitial or vacancy enhanced migration of the implanted dopant atoms. [6] A schematic illustration of the process is given in Fig.…”

Section: And -mentioning

confidence: 99%

Energetic Ion Beams in Semiconductor Processing: Summary of a Doe Panel Study

et al. 1995

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The trend toward smaller dimensions in integrated circuit technology presents severe physical and engineering challenges for ion implantation. These challenges, together with the need for physically-based models at exceedingly small dimensions, are leading to a new level of understanding of fundamental defect science in silicon. Recently the DOE Council on Materials requested that our panel examine the current status and future research opportunities in the area of ion beams in semiconductor processing. Particularly interesting are the emerging approaches to defect and dopant distribution modeling, transient enhanced diffusion, high energy implantation and defect accumulation, and metal impurity gettering. These topics were explored both from the perspective of the emerk&g science issues and the technolou challenges.Ion implantation is one of the most important processing tools in Si integrated circuit technolo=. However, in the move to ever finer dimensions real physical and en_&eering limits are being confronted (see Table I for the Semiconductor Industry Association projections for future device dimensions). For.example, in low energy implantation for shallow junction formation, understanding and controlling defect enhanced diffusion are becoming critical factors in building in the needed dimensional control for circuit design. A panel study commissioned by the U.S. Department of Energy Council on Materials addressed these and other fundamental science and technology issues confronting the semiconductor processing industry in the area of ion beam processing [2]. We briefly summarize those results here. phenomena and the incorporation of this understanding into realistic computer-based models. Coupled with these critical needs in the understanding of the basic processes are the challenges for future commercial machine development in the very low energy and the high energ re, @mes. In addition, focused ion beams are becoming increasingly important, for example in mask repair, while their role in implantation and lithography is still uncertain.In Fig. 1 we illustrate m&y of the key steps where ion implantation is used in the manufacturing of complementary'metal oxide semiconductor (CMOS) technology. Typically 15 or more implantations will be carried out in the course of some 300 or more steps in the

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“…Processes like diffusion of defects under high temperature anneal, as in semiconductor doping, or amorphization processes, that depend on the ion flux can not be modeled using exclusively molecular dynamics. Recent simulations by M. Jaraiz [14] have shown that a good approach for the simulation of ion implantation and annealing at energies on the order of tens of keV, consists on the coupling between binary collision approximation models, like MARLOWE, and Monte Carlo simulations. Following that approach, we use Monte Carlo simulations to study the annealing of defects at high temperatures, and extend the simulation time and scale.…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

“…Clusters larger than 5 have binding energies fitted to a function of the form: Ebv(N) = 3.6-4.9(n2'3 -(n-1)2'3)eV and Eb1(n)=2.5-2. 17(n'2-(n-1)1'2)eV, for V and I of size H respectively, See reference [14] for a description of the program.…”

Section: Modeling Of Defect Diffusion: Monte Cnrlo Simulationsmentioning

confidence: 99%