2003
DOI: 10.1063/1.1623923
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Monolithic multichannel ultraviolet detector arrays and continuous phase evolution in MgxZn1−xO composition spreads

Abstract: We have fabricated Mg x Zn 1Ϫx O epitaxial composition spreads where the composition across the chip is linearly varied from ZnO to MgO. By using a scanning x-ray microdiffractometer and transmission electron microscopy, we have mapped the phase evolution across the spread. We have discovered a unique growth relationship between cubic and hexagonal Mg x Zn 1Ϫx O where their basal planes are coincident in the phase-separated region of the phase diagram where 0.37рx р0.6. The continuously changing band gap acros… Show more

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Cited by 205 publications
(114 citation statements)
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“…In this work, we have concentrated on alloying ZnO by adding Mg and Cd, which allows for variation of the band gap from high in the ultra-violet (UV) down to the green-yellow visible part in the spectrum, respectively. The band gap of Zn 1−x Mg x O can be tuned in the range between 3.4 eV and 7.8 eV (MgO) [19]. Theoretical studies reveal that the increase of the band gap is mostly due to Mg 3s-like states, which shift the CB states to higher values.…”
Section: Band Gap Engineeringmentioning
confidence: 99%
See 1 more Smart Citation
“…In this work, we have concentrated on alloying ZnO by adding Mg and Cd, which allows for variation of the band gap from high in the ultra-violet (UV) down to the green-yellow visible part in the spectrum, respectively. The band gap of Zn 1−x Mg x O can be tuned in the range between 3.4 eV and 7.8 eV (MgO) [19]. Theoretical studies reveal that the increase of the band gap is mostly due to Mg 3s-like states, which shift the CB states to higher values.…”
Section: Band Gap Engineeringmentioning
confidence: 99%
“…For x > 0.6, the alloy crystallizes in rs structure with the band gap changing according to E g (x) = (3.02 + 4.03x) eV [21]. Between x = 0.4 and 0.6, the band gap is not well defined since phase separation occurs [19].…”
Section: Band Gap Engineeringmentioning
confidence: 99%
“…[1][2][3][4][5] Machine learning techniques provide a means to convert the large volume of diverse, complex data collected from materials experiments into actionable knowledge refs 6-18 and have been successfully demonstrated in composition spread experiments. Of particular interest is the use of machine learning techniques to rapidly determine potential composition-phase maps from X-ray diffraction (XRD) data from composition spreads.…”
Section: Introductionmentioning
confidence: 99%
“…5 ' 8 Actually, the in situ work of Takeuchi et al on graded MgZnO films has evidenced the appearance of phase separation for 0.4 < x < 0.6 in the form of Mg-rich precipitated nanograins. 9 The large miscibility gap found between MgO and ZnO under thermal equilibrium limits the máximum Mg contení achievable by chemical deposition techniques, but it can be partially overeóme by using molecular beam epitaxy (MBE), since the growth by MBE can be set far from the alloy equilibrium conditions, the elemental fluxes can be controlled independently, and the oxidation efficieney can be enhanced. 1 In fact, the highest reported Mg contents in Mg^Zn^^O (x ~ 0.5) have been obtained using MBE.…”
Section: Introductionmentioning
confidence: 99%