For more information about the Spectral Barcoding and establishing structure-spectra relationship in quantum dots, see the following publications: - Vladan Mlinar and Alex Zunger, Phys. Rev. B 80, 035328 (2009). - Vladan Mlinar et al. Phys. Rev. B 80, 165425 (2009). -------------------------- My full publications list can be found at: www.vladanmlinar.com/publications.html

1. Deciphering Structural Information from the
Multiexcitonic Spectra of a Quantum Dot
Vladan Mlinar & Alex Zunger
National Renewable Energy Laboratory
Golden, Colorado USA
Vladan.Mlinar@nrel.gov

2. QDs: Structure - Spectra relationship
Methods for structural characterization Single-dot spectroscopy
• TEM based methods
• X-ray diffraction
• X-STM

3. QDs: Structure - Spectra relationship
Methods for structural characterization Single-dot spectroscopy
• TEM based methods
• X-ray diffraction
• X-STM
(M. Bozkurt, J. M. Ulloa, & P. M. Koenraad)
• No atomic resolution
• All of the methods require assumption
about composition profile and/or shape!

4. QDs: Structure - Spectra relationship
Methods for structural characterization Single-dot spectroscopy
• TEM based methods
• X-ray diffraction
• X-STM
(M. Ediger &
R. J. Warburton)
(M. Bozkurt, J. M. Ulloa, & P. M. Koenraad)
• No atomic resolution
• All of the methods require assumption
about composition profile and/or shape!

5. QDs: Structure - Spectra relationship
Methods for structural characterization Single-dot spectroscopy
• TEM based methods
• X-ray diffraction
• X-STM
(M. Ediger &
R. J. Warburton)
(M. Bozkurt, J. M. Ulloa, & P. M. Koenraad)
• No atomic resolution • Controllable number of electrons and holes
• All of the methods require assumption • μeV resolution
about composition profile and/or shape!

6. Typically, Structure is used to predict Spectra
Assume Calculate
or resulting
measure spectra
structure
• Since for quantum dots we do not know the structure:
Measured
emission Structure
spectra

7. Typically, Structure is used to predict Spectra
Assume Calculate
or resulting
measure spectra
structure
• Since for quantum dots we do not know the structure:
Measured
emission Structure
spectra
Is this possible?

8. Question: What is the structural information
encoded in the multiexcitonic spectra of a QD?
?

9. Spectral Barcoding vs. DNA Barcoding:
Barcoding
Barcoder
Organism is
identified as
belonging to a
particular species
Sci. Am. p. 82-88 (October 2008)

10. Spectral Barcoding vs. DNA Barcoding:
Barcoding
Barcoder
Organism is
identified as
belonging to a
particular species
Sci. Am. p. 82-88 (October 2008)

11. Spectral Barcoding vs. DNA Barcoding:
Barcoding
Barcoder
Organism is
identified as
belonging to a
particular species
? QD is identified as
belonging to a
group of QDs with
common structural
motifs.
Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).

12. How does the Spectral Barcoding work?
Spectral barode:
Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).

13. How does the Spectral Barcoding work?
Spectral barcoding
procedure
Spectral barode:
Artificial Intelligence QD library
(Distilling rules from library) Deterministic links between
structures and spectral marker
Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).

14. How does the Spectral Barcoding work?
Spectral barcoding
procedure
Spectral barode:
Artificial Intelligence QD library
(Distilling rules from library) Deterministic links between
structures and spectral marker
RESULT: a set
Structural Motifs:
of QD structural
motifs! Structure • h = 2 – 3nm
• Xav(In) = 75-80%
Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).

15. Spectral Barcoding: Data-mining of the library
QD structure is discretized into a set of Ns=5 structural motifs, each taking up one of
Nv possible values:
Motifs: Shape b (nm) h (nm) XIn (%) profile
Trun.Cone 12 2.0 50 Homog.
Trun. Pyr. 18 3.0 60 Linear
Lens 20 3.5 70
Elong. 23 4.0 80
Lens [110]
Elong. 25 5.0 90
Lens [110]
Elong. 30 6.0 100
Lens [100]
Structure

16. Spectral Barcoding: Data-mining of the library
QD structure is discretized into a set of Ns=5 structural motifs, each taking up one of
Nv possible values:
Motifs: Shape b (nm) h (nm) XIn (%) profile
Trun.Cone 12 2.0 50 Homog.
Trun. Pyr. 18 3.0 60 Linear
Lens 20 3.5 70
Elong. 23 4.0 80
Lens [110]
Elong. 25 5.0 90
Lens [110]
Elong. 30 6.0 100
Lens [100]
Bayesian Data Reduction Algorithm:
Structure
• Training: Testing how each structural motif and its corresponding values influences the
barcode
• Result: Identifies the set of structural motifs that are responsible for a given spectral
barcode sequence.

17. Spectral Barcoding: Consistency test!
Vladan Mlinar and Alex Zunger,
PRB 80, 035328 (2009).

18. Spectral Barcoding: Consistency test!
Vladan Mlinar and Alex Zunger,
PRB 80, 035328 (2009).

19. Spectral Barcoding: Consistency test!
Vladan Mlinar and Alex Zunger,
PRB 80, 035328 (2009).

20. Spectral Barcoding: Consistency test!
Validation!
Vladan Mlinar and Alex Zunger,
PRB 80, 035328 (2009).

21. Question: How does the deduced structure
relates to the “real structure”?

22. Spectral Barcoding: Why is it important?
Collaboration with
three experimental
groups! Structural Characterization by X-STM
Quantum Dot Theory
growth
Many body
pseudopotential
calculations
Single-dot Spectroscopy Calculated spectra
Antonio Badolato
(ETH Zurich, Switzerland)

23. Spectral Barcoding: Why is it important?
Collaboration with
three experimental
groups! Structural Characterization by X-STM
Quantum Dot Theory
growth
M. Bozkurt, J. M. Ulloa, & P. M. Koenraad
(TU Eindhoven, The Netherlands) Many body
pseudopotential
calculations
Single-dot Spectroscopy Calculated spectra
Antonio Badolato
(ETH Zurich, Switzerland)

24. Spectral Barcoding: Why is it important?
Collaboration with
three experimental
groups! Structural Characterization by X-STM
Quantum Dot Theory
growth
M. Bozkurt, J. M. Ulloa, & P. M. Koenraad
(TU Eindhoven, The Netherlands) Many body
pseudopotential
calculations
Single-dot Spectroscopy Calculated spectra
Antonio Badolato
(ETH Zurich, Switzerland)
M. Ediger & R. J. Warburton
(Heriot-Watt University, UK)

25. Spectral Barcoding: Why is it important?
Collaboration with
three experimental
groups! Structural Characterization by X-STM
Quantum Dot Theory
growth
M. Bozkurt, J. M. Ulloa, & P. M. Koenraad
(TU Eindhoven, The Netherlands) Many body
pseudopotential
calculations
Single-dot Spectroscopy Calculated spectra
Antonio Badolato
(ETH Zurich, Switzerland)
M. Ediger & R. J. Warburton XS-2 < XT-2 < X-1 < XX0 < X0 sequence
(Heriot-Watt University, UK) in measured spectra from each and
every QD studied in the ensemble is
kept.

26. Spectral Barcoding: Why is it important?
Collaboration with
three experimental
groups! Structural Characterization by X-STM
Quantum Dot Theory
growth
M. Bozkurt, J. M. Ulloa, & P. M. Koenraad
(TU Eindhoven, The Netherlands) Many body
pseudopotential
calculations
Single-dot Spectroscopy
? Calculated spectra
V. Mlinar, G. Bester, &
A. Zunger (NREL)
Antonio Badolato
(ETH Zurich, Switzerland)
• Exciton energies
M. Ediger & R. J. Warburton XS-2 < XT-2 < X-1 < XX0 < X0 sequence • XS-2 < XT-2 < X-1 < XX0 < X0
(Heriot-Watt University, UK) in measured spectra from each and sequence
every QD studied in the ensemble is
kept. Vladan Mlinar et al., PRB 80, 165425 (2009).

27. XSTM→Theory→Spectroscopy Fails to Close Loop!
• Exciton Energies:
Calculated: 1.05 -1.12 eV
Structure Measured: 1.08-1.09 eV
Vladan Mlinar et al., PRB 80, 165425 (2009).

28. XSTM→Theory→Spectroscopy Fails to Close Loop!
• Spectral Hard Rules:
EXP. XS-2 < XT-2 < X-1 < XX0 < X0
Structure
Model 1 XS-2 < X0 < XX0 < X-1 < XT-2
Model 2 XS-2 < X0 < XX0 < XT-2 < X-1
Model 3 X0 < XX0 < XS-2 < X-1 < XT-2
Model 4 X0 < XS-2 < XX0 < X-1 < XT-2
Model 5 XS-2 < XX0 < X0 < X-1 < XT-2
All five XSTM deduced Model QDs
violate Spectroscopic Hard rules!
Vladan Mlinar et al., PRB 80, 165425 (2009).

29. Structural motifs underlying Spectral Hard Rule:
INPUT:
Spectral barcoding
Procedure
Vladan Mlinar et al., PRB 80, 165425 (2009).

30. Structural motifs underlying Spectral Hard Rule:
INPUT:
Spectral barcoding
Procedure
OUTPUT:
Primary structural
Motifs
1. Height (h)
2. Base-length (b)
3. Average In
composition (XIn) Vladan Mlinar et al., PRB 80, 165425 (2009).

31. Spectroscopy→Theory→Structure closes the Loop!

32. Spectroscopy→Theory→Structure closes the Loop!
• More than one dot can be constructed!
• Spectral Hard Rules are satisfied by
the construction!
Vladan Mlinar et al., PRB 80, 165425 (2009).

33. Conclusions:
Spectral Barcoding: Procedure for deciphering structural motifs
from the multiexcitonic spectra
• We established missing structural basis for QD spectroscopy
• We offer spectroscopically-derived structural motifs that combined with
X-STM measurements give more realistic QD structure.
Vladan Mlinar and Alex Zunger, PRB 80, 035328 (2009).
Vladan Mlinar et al., PRB 80, 165425 (2009).
Thank you for your attention!

34. Basic Paradigm of Spectroscopy of Molecules
• To understand the spectra one must know the structure
(hence symmetry) of the molecule
• Structure-spectra relationship in molecules has historically been
facilitated by the accumulated knowledge on electronic and vibrational
spectral fingerprints of specific groups making up the molecules
• Deliberate design of molecules with given properties
Structure

35. Spectroscopic vs. Geometrical QD size:
Can we construct a model QD that has geometrical size as extracted from XSTM, but
spectroscopic size as deduced by spectral barcoding?

36. XSTM deduced Model QDs:
Model 1 Model 2 Model 3 Model 4
• Truncated cone • Truncated pyramid • Truncated pyramid • Ellipsoid
• No wetting layer • No wetting layer • No wetting layer • No wetting layer
Model 5
• Truncated cone
• Includes wetting layer