Band gap engineering in Al, Cu, N and Al/Cu Co-doped ZnO thin films: experimental study and quantum machine learning...

Area of Science:

• Materials Science
• Condensed Matter Physics
• Quantum Computing

Background:

• Zinc oxide (ZnO) thin films are crucial for optoelectronic applications.
• Tuning ZnO's properties, particularly its band gap, is essential for device performance.
• Traditional methods for optimizing materials can be time-consuming.

Purpose of the Study:

• To investigate the effects of doping (Al, Cu, N, Al/Cu) and annealing on ZnO thin film properties.
• To develop a quantum machine learning model for predicting ZnO band gaps.
• To establish an integrated framework for accelerated band gap engineering of ZnO.

Main Methods:

• RF magnetron sputtering for ZnO thin film deposition.
• Post-deposition annealing at temperatures ranging from 300-600 °C.
• Characterization using X-ray diffraction (XRD), energy-dispersive spectroscopy (EDS), and atomic force microscopy (AFM).
• Optical band gap determination via UV-Vis spectroscopy and Tauc analysis.
• Development and application of a variational quantum regression (VQR) model.

Main Results:

• Structural, compositional, and morphological properties were analyzed.
• Optical band gaps were experimentally determined.
• Key material descriptors (lattice constants, crystallite size, roughness, Urbach energy) were identified.
• The VQR model achieved a mean squared error of 0.0576 eV in predicting band gaps.
• The model accurately captured trends influenced by dopant type and annealing temperature.

Conclusions:

• The study successfully combined experimental tuning with quantum-enhanced predictive modeling.
• An integrated framework for accelerated band gap engineering of ZnO thin films was demonstrated.
• The findings have direct implications for optimizing transparent conductive oxides and other optoelectronic materials.