Algorithm-Based Linearly Graded Compositions of GeSn on GaAs (001) via Molecular Beam Epitaxy

Area of Science:

• Materials Science
• Semiconductor Growth
• Nanotechnology

Background:

• Future advancements in high-composition Germanium-Tin (GeSn) films are anticipated to be driven by algorithmic approaches.
• Precise control over composition is crucial for tailoring the optoelectronic properties of GeSn alloys.

Purpose of the Study:

• To demonstrate the efficacy of a logarithmic-based algorithm for growing high-quality GeSn films.
• To achieve high GeSn compositions (up to 16%) on Gallium Arsenide (GaAs) (001) substrates using molecular beam epitaxy (MBE).

Main Methods:

• Utilized composition targeting and logarithmic tin (Sn) cell temperature control.
• Employed molecular beam epitaxy (MBE) for film deposition.
• Characterized films using X-ray diffraction (XRD), simulation, secondary ion mass spectrometry (SIMS), and atomic force microscopy (AFM).

Main Results:

• Successfully achieved linearly graded pseudomorphic Ge_1-xSn_x compositions up to 10% Sn.
• Continued the compositional gradient to 16% GeSn after partial structural relaxation.
• Demonstrated algorithmically driven, linearly graded GeSn film growth.

Conclusions:

• The developed logarithmic algorithm provides precise control for growing high-composition GeSn films.
• This methodology represents a significant advancement in GeSn material development.
• Paved the way for algorithmically controlled growth of advanced semiconductor alloys.