On the possible identification of defects using the autocorrelation function approach in double Doppler broadening of annihilation radiation spectroscopy
Cd. Beling et al., On the possible identification of defects using the autocorrelation function approach in double Doppler broadening of annihilation radiation spectroscopy, J PHYS-COND, 10(46), 1998, pp. 10475-10492
The recent revived interest in the use of double-Doppler broadening of anni
hilation radiation (D-DBAR) spectroscopy, which employs two Ga detectors in
back-to-back geometry, has stemmed mainly from its potential in defect ide
ntification as a result of its elemental sensitivity through core annihilat
ions in atoms at the defect site. Emphasis has thus largely concentrated on
the high momentum spectral range. In contrast the present work emphasizes
the need to also focus attention on the low momentum region of the D-DBAR s
pectra. It is argued that the root 2 improved resolving power of D-DBAR, in
conjunction with spectral deconvolution, should give future 1D (one dimens
ional) momentum data approaching in quality those obtainable using 1D-ACAR
(angular correlation of annihilation radiation), thus forming an alternativ
e technique for observing the structure containing diffraction patterns tha
t originate from annihilations with localized electron states at positron t
rapping defects. Rotation of the sample about a specified crystal axis, and
the binning of events by angle, is suggested as a means of extending the t
echnique to form a 2D- (two dimensional) DEAR counterpart to 2D-ACAR. The a
dvantages of considering the real space positron electron wavefunction prod
uct AF (autocorrelation function), obtained by simple manipulation of the D
-DBAR data in Fourier space, are outlined. In particular the possible visua
lization offered in real space of a defect's physical geometry, with the pr
ospect of building up a library of contour patterns for future defect ident
ification, is discussed, taking the silicon monovacancy in Si and the negat
ive As vacancy in GaAs as examples.