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Genetically Engineered Nanostructure Devices

Published online by Cambridge University Press:  10 February 2011

Gerhard Klimeck ,
Carlos H. Salazar-Lazaro ,
Adrian Stoica  and
Thomas Cwik
Gerhard Klimeck
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109
Carlos H. Salazar-Lazaro
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109
Adrian Stoica
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109
Thomas Cwik
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109
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Abstract

Material variations on an atomic scale enable the quantum mechanical functionality of devices such as resonant tunneling diodes (RTDs), quantum well infrared photodetectors (QWIPs), quantum well lasers, and heterostructure field effect transistors (HFETs). The design and optimization of such heterostructure devices requires a detailed understanding of quantum mechanical electron transport. The Nanoelectronic Modeling Tool (NEMO) is a general-purpose quantum device design and analysis tool that addresses this problem. NEMO was combined with a parallelized genetic algorithm package (PGAPACK) to optimize structural and material parameters. The electron transport simulations presented here are based on a full band simulation, including effects of non-parabolic bands in the longitudinal and transverse directions relative to the electron transport and Hartree charge self-consistency. The first result of the genetic algorithm driven quantum transport calculation with convergence of a random structure population to experimental data is presented.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 551: Symposium JJ – Materials in Space-Science, Technology & Exploration , 1998 , 149
Copyright
Copyright © Materials Research Society 1999

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References

Billiography

1 NEMO, Nanoelectronic Modeling, in http://www.raytheon.com/rtis/nemo/.Google Scholar
2 Lake, R. et al. , J. Appl. Phys., 81(12), 7845 (1997).Google Scholar
3 Lake, R. et al. , phys. stat. sol. (b), 204, 354 (1997).Google Scholar
4 Klimeck, G. et al. , Appl. Phys. Lett., 67(17), 2539 (1995).Google Scholar
5 Klimeck, G. et al. , IEEE DRC, 1997: p. 92.Google Scholar
6 Bowen, R. C. et al. , J. Appl. Phys., 81, 3207 (1997).Google Scholar
7 Seabaugh, A. C., Texas Instruments, private communication, 1997.Google Scholar
8 The actual CPU time needed for a single I-V simulation depends strongly on the choice of material systems, bandstructure models, temperature scattering models, and bias points. The individual I-V characteristics presented here take about 30 minutes to compute on a single 200MHz R10000 CPU of an SGI Origin.Google Scholar
9 Levine, D., http://www-unix.mcs.anl.gov/∼levine/PGAPACK/index.html, Parallel Genetic Algorithm Library.Google Scholar
10 Schulman, J. N., Second Workshop on Characterization, Future Opportunities and Applications of 6.1 Å III-V Semiconductors, Aug. 24-26, 1998, Naval Research Laboratory, Washington, DC, http://estd-www.nrl.navy.mil/code6870/code6870.html. H. C. Liu, J. Appl. Phys. 64, 4792 (1988).. H. C. Liu, J. Appl. Phys. 53, 485 (1988).Google Scholar
11 PGAPACK is implemented with MPI where N-1 of N processors are slaves to one master processor. The master takes care of the collection of data from the slaves. In a cluster of 64 CPU's we therefore renew only 63 genes in every generation.Google Scholar
12 The mutation operations may drive a particular parameter outside its original range. Therefore the full parameter space may not be limited.Google Scholar