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8 April 1998 Computational materials science: an increasingly reliable engineering tool (example: defects in HgCdTe alloys)
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Computational materials science has evolved in recent years into a reliable theory capable of predicting not only idealized materials and device performance properties, but also those that apply to practical engineering developments. The codes run on workstations and even now are fast enough to be useful design tools. A review will be presented of the current status of this rapidly advancing field.As a demonstration of the power of the methods, predictions of the native point and complex defect, and impurity densities for the Hg0.8Cd0.2Te alloy as functions of external processing conditions will be treated. Where measurements have been done, the observed values agree well with the predictions. As an example, we find that As incorporates predominately on the cation sublattice, if the material is grown form the Te side of the existence curve, whereas it tends to reside on the anion sublattice in Hg-saturated growth. On the cation sublattice As is a donor. It is an acceptor on the Te sublattice. We have devised a post-MBE- growth processing method to encourage the transfer of As form the cation to the anion sublattice. Those aspects of the proposed process that have been tested work.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arden Sher, M. van Schilfgaarde, and M. A. Berding "Computational materials science: an increasingly reliable engineering tool (example: defects in HgCdTe alloys)", Proc. SPIE 3287, Photodetectors: Materials and Devices III, (8 April 1998);

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