Hostname: page-component-7bb8b95d7b-l4ctd Total loading time: 0 Render date: 2024-09-13T09:17:22.587Z Has data issue: false hasContentIssue false

Modeling Evolution of Temperature, Stress, Defects, and Dopant Diffusion in Silicon During Spike and Millisecond Annealing

Published online by Cambridge University Press:  01 February 2011

Victor Moroz
Affiliation:
[email protected], Synopsys, TCAD, 700 East Middlefield Road, Mountain View, CA, 94043, United States
Ignacio Martin-Bragado
Affiliation:
[email protected], Synopsys, Mountain View, CA, 94043, United States
Nikolas Zographos
Affiliation:
[email protected], Synopsys, Zurich, N/A, Switzerland
Dmitri Matveev
Affiliation:
[email protected], Synopsys, Zurich, N/A, Switzerland
Christoph Zechner
Affiliation:
[email protected], Synopsys, Zurich, N/A, Switzerland
Munkang Choi
Affiliation:
[email protected], Synopsys, Mountain View, CA, 94043, United States
Get access

Abstract

The bulk CMOS devices continue as the dominant player for at least another couple of technology nodes. This drives the increasingly contradicting requirements for the channel, source/drain extension, and heavily doped source/drain doping profiles. To analyze and optimize the transistors, it is becoming necessary to combine a number of effects that have been treated as decoupled so far. The temperature gradients, combined with stress engineering techniques such as embedded SiGe and Si:C source/drain and stress memorization technique, create non-uniform stress distributions determined by the layout patterns. The interaction of implant-induced damage with dopants, stress, and defect traps shapes up the dopant activation, retention of useful stress, and junction leakage. This work reviews recent trends in modeling these effects using continuum and kinetic Monte Carlo methods.

Type
Research Article
Copyright
Copyright © Materials Research Society 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1] EMW User's Manual, v. 2007.12, Synopsys Inc., 2007.Google Scholar
[2] Sentaurus Process User's Manual, v. 2007.12, Synopsys Inc., 2007.Google Scholar
[3] Jaraiz, M. et al., “Atomistic Front-End Process Modelling: A Powerful Tool for Deep-Submicron Device Fabrication,” SISPAD 2001 Proceedings, Springer-Verlag, pp. 1017, 2001.Google Scholar
[4] Martin-Bragado, Ignacio et al. “Modeling charged defects, dopant diffusion and activation mechanisms for TCAD simulations using kinetic Monte Carlo,” Nuclear Instruments and Methods in Physics Research B, v. 253, pp. 6367, 2006.Google Scholar
[5] Kuhn, K., “Reducing Variation in Advanced Logic Technologies: Approaches to Process and Design for Manufacturability of Nanoscale CMOS,” IEDM Technical Digest, pp. 471474, 2007.Google Scholar
[6] Sharp, J. et al., “Deactivation of Ultra Shallow B and BF2 Profiles After Non-Melt Laser Annealing,” Materials Research Society Symposium Proceedings, v. 912, 2006.Google Scholar
[7] Tsuno, H. et al., “Advanced Analysis and Modeling of MOSFET Characteristic Fluctuation Caused by Layout Variation,” VLSI Technology Symposium Technical Digest, pp. 204205, 2007.Google Scholar
[8] Moroz, V. et al., “Physical Modeling of Defects, Dopant Activation and Diffusion in Aggressively Scaled Bulk and SOI Devices: Atomistic and Continuum Approaches,” Materials Research Society Symposium Proceedings, 2006.Google Scholar
[9] Moroz, V. et al., “Suppressing Layout-Induced Threshold Variations by Halo Engineering,” Electrochemical Society Conference Proceedings, 2005.Google Scholar
[10] Seismos User's Manual, v. 2008.03, Synopsys Inc., 2008.Google Scholar