Secondary ion mass spectrometry characterization of the diffusion properties of 17 elements implanted into silicon

Francois‐Saint‐Cyr, Hugues; Anoshkina, E.; Stevie, F. A.; Chow, Lee; Richardson, Kathleen; Zhou, Dongmei

doi:10.1116/1.1396638

Cited by 51 publications

(26 citation statements)

References 14 publications

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“…2, see also [7]). It seems that, in the case of prolonged (5 hr) annealing under 10 5 Pa and contrary to earlier observation for the shorter time processed Si:Mn samples [7,15], part of Mn atoms at the > 0.25 μm depth remains at the same position as these in the as implanted sample (Fig. 2].…”

Section: Resultscontrasting

confidence: 45%

“…The treatment under HP results in enhanced X-ray diffuse scattering intensity from Si:Cr evidencing the presence of crystallographic defects within c-Si formed in effect of partial SPER of a-Si [9]. Possibly this is related to a little higher diffusivity of Cr in Si (~ 10 -7 cm 2 s -1 and 2.1x10 -6 cm 2 s -1 , respectively, for V and Cr at 1270 K [15]). …”

Section: Resultsmentioning

confidence: 99%

“…2]. Possibly this is related to comparatively high solubility of Mn in Si (3x10 16 cm -3 and 3x10 14 cm -3 , respectively, for Mn and Cr at 1270 K [15]) and Mn gettering on some implantation -induced defects at the depth exceeding R p . Processing of Si:V, Si:Cr and Si:Mn samples at ≤ 723 K can result in detectable ferromagnetic ordering (see also [7 -9]).…”

Section: Resultsmentioning

confidence: 99%

“…â³äïîâ³äíèõ ìåòàëåâèõ ³îí³â (äîçè 1x10 15 -1x10 16 ñì 2 , ç åíåðã³ºþ 160 êåÂ àáî 200 êåÂ) ó (001) îð³ºíòîâàíèé Si, ÿêèé âèðîùåíî ìåòîäîì ×îõðàëüñêîãî.…”

Section: Introductionunclassified

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Silicon Based Materials for Application in Spintronics

Misiuk

Chow

Barcz³

et al. 2007

SEMST

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SILICON BASED MATERIALS FOR APPLICATION IN SPINTRONICSAImplantation produces amorphous silicon (a-Si) near the implanted ions range. Quasi -epitaxial re -growth of a-Si takes place at HT. The V, Cr and Mn concentration profiles do not depend markedly on HP if applied below 1000 K. Marked diffusion of implanted atoms toward the sample surface is observed in the case of processing at > 1000 K under 10 5 Pa, especially in the case of Si:Cr and Si:Mn. Under HP this diffusion is even more pronounced, re-crystallization of a-Si is retarded and the a-Si / Si interface becomes enriched with metallic atoms.Processing of Si:V, Si:Cr and Si:Mn at ≤ 723 K results in distinct ferromagnetic ordering, detectable also above 50 K. This means that the new Si-V, Si-Cr and Si-Mn materials belonging to the family of Diluted Magnetic Semiconductors may be produced.

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Section: Resultscontrasting

confidence: 45%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionunclassified

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Silicon Based Materials for Application in Spintronics

Misiuk

Chow

Barcz³

et al. 2007

SEMST

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“…However, the high-resolution structural characterization of the investigated Si:Mn systems (both epitaxially grown and ion-implanted) was rather limited. The diffusion of Mn in Si upon annealing was only investigated by Rutherford backscattering (RBS) and secondary ion mass spectrometry [9,10].…”

Section: Introductionmentioning

confidence: 99%

Structural and magnetic properties of Mn-implanted Si

et al. 2007

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Structural and ferromagnetic properties in Mn implanted, p-type Si were investigated. High resolution structural analysis techniques like synchrotron X-ray diffraction revealed the formation of MnSi 1.7 nanoparticles already in the as implanted samples. Depending on the Mn-fluence, the size increases from 5 nm to 20 nm upon rapid thermal annealing. No significant evidence is found for Mn substituting Si sites either in the as-implanted or annealed samples. The observed ferromagnetism yields a saturation moment of 0.21 μ B per implanted Mn at 10 K, which could be assigned to MnSi 1.7 nanoparticles as revealed by a temperature dependent magnetization measurement.

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Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence

Elbaz

Furman

Toroker

2019

Adv Funct Materials

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Diffusion describes the stochastic motion of particles and is often a key factor in determining the functionality of materials. Modeling diffusion of atoms can be very challenging for heterogeneous systems with high energy barriers. In this report, popular computational methodologies are covered to study diffusion mechanisms that are widely used in the community and both their strengths and weaknesses are presented. In static approaches, such as electronic structure theory, diffusion mechanisms are usually analyzed within the nudged elastic band (NEB) framework on the ground electronic surface usually obtained from a density functional theory (DFT) calculation. Another common approach to study diffusion mechanisms is based on molecular dynamics (MD) where the equations of motion are solved for every time step for all the atoms in the system. Unfortunately, both the static and dynamic approaches have inherent limitations that restrict the classes of diffusive systems that can be efficiently treated. Such limitations could be remedied by exploiting recent advances in artificial intelligence and machine learning techniques. Here, the most promising approaches in this emerging field for modeling diffusion are reported. It is believed that these knowledge-intensive methods have a bright future ahead for the study of diffusion mechanisms in advanced functional materials.observed in liquids, gases, and solid-state materials. One of the main cornerstones for the macroscopic understanding of diffusion was laid down in the mid-19th century by Adolf Fick. By observing Graham's experiments in gases and salt water, and by the analogy between Fourier's law of thermal conduction and diffusion, Fick developed two equations known as the Fick's laws , , , J D C x y z t

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Secondary ion mass spectrometry characterization of the diffusion properties of 17 elements implanted into silicon

Cited by 51 publications

References 14 publications

Silicon Based Materials for Application in Spintronics

Silicon Based Materials for Application in Spintronics

Structural and magnetic properties of Mn-implanted Si

Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence

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