Less than 10 defects/cm2 · μm in molecular beam epitaxy grown GaAs by arsenic cracking

Izumi, Satoshi; Hayafuji, N.; Sonoda, T.; Takamiya, S.; Mitsui, S.

doi:10.1016/0022-0248(95)80171-8

Cited by 16 publications

(7 citation statements)

References 16 publications

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“…The deposition technology of choice for such heterostructures, in particular those with quantum wells or superlattices, is typically molecular beam epitaxy (MBE) due to the high layer thickness precision (single monolayer) and ability to form abrupt interfaces. While high quality growth techniques and appropriate equipment considerations have been shown to reduce the surface defect density of MBE-grown GaAs to only few per square centimeter [2,3], defect densities in GaSb remain comparatively high.…”

Section: Introductionmentioning

confidence: 99%

Optimization of MBE-grown GaSb buffer layers and surface effects of antimony stabilization flux

Koerperick

Murray

Norton

et al. 2010

Journal of Crystal Growth

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Section: Introductionmentioning

confidence: 99%

Optimization of MBE-grown GaSb buffer layers and surface effects of antimony stabilization flux

Koerperick

Murray

Norton

et al. 2010

Journal of Crystal Growth

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“…Since defects are often responsible for device failure (and consequently, reduced circuit yield), retaining low defect density is crucial [11]. A well-known in situ cleaning technique involves heating the substrate to desorb the native oxide on the substrate surface.…”

Section: ) Physical Interpretationsmentioning

confidence: 99%

“…In terms of the static responses, surface morphology has always been one of the primary concerns in MBE growth [11]. One measure of surface morphology is defect density on the surface.…”

Section: Process Characterizationmentioning

confidence: 99%

Using neural networks to construct models of the molecular beam epitaxy process

Lee

Brown

Dagnall

et al. 2000

IEEE Trans. Semicond. Manufact.

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This paper presents the systematic characterization of the molecular beam epitaxy (MBE) process to quantitatively model the effects of process conditions on film qualities. A fivelayer, undoped AlGaAs and InGaAs single quantum well structure grown on a GaAs substrate is designed and fabricated. Six input factors (time and temperature for oxide removal, substrate temperatures for AlGaAs and InGaAs layer growth, beam equivalent pressure of the As source and quantum well interrupt time) are examined by means of a fractional factorial experiment. Defect density, X-ray diffraction, and photoluminescence are characterized by a static response model developed by training back-propagation neural networks. In addition, two novel approaches for characterizing reflection high-energy electron diffraction (RHEED) signals used in the real-time monitoring of MBE are developed. In the first technique, principal component analysis is used to reduce the dimensionality of the RHEED data set, and the reduced RHEED data set is used to train neural nets to model the process responses. A second technique uses neural nets to model RHEED intensity signals as time series, and matches specific RHEED patterns to ambient process conditions. In each case, the neural process models exhibit good agreement with experimental results.Index Terms-Molecular beam epitaxy (MBE), neural networks, process modeling.

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“…A change in As/Ga flux ratio is also known to affect the defect density [9]. Although not well understood, the potential reasons for ODs to appear are: (1) gallium spitting onto gallium crucible walls [10], (2) the formation of gallium oxides on the surface [11], (3) uncontrolled impurities in the arsenic source [12], (4) substrate contamination [13,14], and (5) presence of stacking faults [15]. With these potential causes in mind, special care with arsenic and gallium molecular beams and the transport of substrates has made it possible to reduce the OD density down to 10 2 cm À2 [16].…”

Section: Introductionmentioning

confidence: 98%