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Intelligent Engineering Systems through Artificial Neural Networks, Volume 16

Editor
Cihan H. Dagli
Cihan H. Dagli
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Anna L. Buczak
Anna L. Buczak
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David L. Enke
David L. Enke
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Mark Embrechts
Mark Embrechts
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Okan Ersoy
Okan Ersoy
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ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006

InAs(GaSb)/AlGaAsSb devices are promising semiconductor nanosystems with signal generation and detection capabilities in the near-infrared (NIR) window of the electromagnetic spectrum. A major obstacle with synthesis of these devices is the ability to accurately achieve and control the broad range of composition and nanoscale features in complex, multi-layer structures. The sources of variance can be attributed to artifacts at the nanoscale of the device structure. Based on numerous experiments and neural network modeling techniques, our results reveal relationships between the growth parameters, material properties, and device performance parameters. These results can be used to evaluate the growth process and these complex nanostructures.

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