Insulator-to-semiconductor conversion of solution-processed ultra-wide bandgap amorphous gallium oxide via hydrogen annealing

Purnawati, Diki; Bermundo, Juan Paolo; Uraoka, Yukiharu

doi:10.35848/1882-0786/ac466a

Cited by 8 publications

(25 citation statements)

References 27 publications

(30 reference statements)

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“…This is due to H doping introduction which acts as a shallow donor and further improve the electron carrier concentration. Additional states below CBM (donor states) were created and narrowed the Eg [4,9].…”

Section: -2 / D Purnawati • Student Papermentioning

confidence: 99%

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31‐2: Student Paper: Fermi Level Prediction of Solution‐processed Ultra‐wide Band gap a‐Ga₂O_x via Supervised Machine Learning Models

Purnawati

Regonia

Bermundo

et al. 2022

Symp Digest of Tech Papers

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This work presents machine learning (ML) assisted Fermi level prediction of solution‐processed ultra‐wide bandgap (UWB) amorphous gallium oxide (a‐Ga2Ox) which can significantly accelerate the fabrication of semiconducting UWB a‐Ga2Ox‐based material for future display application. Different models such as Kernel Ridge Regression (KRR), Support Vector Regression (SVR) and Random Forest Regression (RFR) were trained with empirical features, including experimental thickness, annealing temperature and environment during the solution‐processed UWB a‐Ga2Ox film fabrication. This work is a big step towards rapid and cost‐effective optimization method of fabricating UWB a‐Ga2Ox‐based devices.

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Section: -2 / D Purnawati • Student Papermentioning

confidence: 99%

“…It can be either narrowing or widening [3]. In other words, bandgap engineering can be performed to increase the carrier concentration which implies EF shifting closer to CBM [4]. However, experimental methods need complex optimization and time-consuming to obtain the optimize experimental parameters.…”

Section: Introductionmentioning

confidence: 99%

31‐2: Student Paper: Fermi Level Prediction of Solution‐processed Ultra‐wide Band gap a‐Ga₂O_x via Supervised Machine Learning Models

Purnawati

Regonia

Bermundo

et al. 2022

Symp Digest of Tech Papers

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“…Ultrawide-bandgap (UWB) oxide semiconductors have gained attention for optoelectronic device applications due to their unique combination of electrical conductivity and optical transparency. − Among various UWB materials, amorphous gallium oxide (a-Ga 2 O x ) is the most superb candidate for future device applications due to its UWB ( E g > 4.0 eV), excellent uniformity and transparency, high breakdown voltage, and flexibility in device design . It has been widely used in various devices such as thin-film transistors (TFTs), , sensors, and solar-blind photodetectors (SBPDs). − In particular, a-Ga 2 O x SBPDs exhibit considerable device performance as compared to β-Ga 2 O 3 . In selected cases, a-Ga 2 O x -based SBPDs exhibit higher responsivity than their β-Ga 2 O 3 counterpart. − Fast response speeds in a-Ga 2 O x SBPDs approaching those of single-crystal Ga 2 O 3 have also been demonstrated through reduction of oxygen vacancies, which act as deep traps in a-Ga 2 O x and consequently induce a persistent photoconductivity effect .…”

Section: Introductionmentioning

confidence: 99%

“…One method to measure the n of materials is through Hall effect measurement. However, due to equipment limitations, it was found that measuring n for UWB a-Ga 2 O x material is problematic . Relatively low n and high resistivity of a-Ga 2 O x make it difficult to measure.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learned Fermi Level Prediction of Solution-Processed Ultrawide-Bandgap Amorphous Gallium Oxide (a-Ga₂O_x)

Purnawati

Regonia

Bermundo

et al. 2022

ACS Appl. Electron. Mater.

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The Fermi level (E F ) relative position to the conduction band minimum is a crucial consideration for controlling electrical conductivity and semiconductor device performance in thin-film transistors, sensors, and photodetectors. Experiment complexity and expensive material resources for predicting E F via an experimental approach render a machine learning (ML) approach to be more appropriate. This work presents ML-assisted E F prediction of solution-processed ultrawide-bandgap (UWB) amorphous gallium oxide (a-Ga 2 O x ). Three regression models�kernel ridge regression, support vector regression, and random forest regression�were trained with experimental features including the film thickness, baking temperature, and gas environment during solution deposition of the a-Ga 2 O x film. The results show that ML models can be used to predict E F of the UWB a-Ga 2 O x film and also identify optimized fabrication parameters to achieve the optimized E F . Moreover, the ML approach can significantly accelerate the fabrication of semiconducting UWB a-Ga 2 O x -based material for future device applications. This work is a big step toward rapid and cost-effective optimization methods for developing UWB a-Ga 2 O x -based devices.

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Colloidal Quantum Dots: Synthesis, Composition, Structure, and Emerging Optoelectronic Applications

Zhang

et al. 2022

Laser & Photonics Reviews

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In recent decades, quantum dots (QDs) with tunable bandgap, large absorption coefficient, high quantum yield, multiexciton effect, and easy solution processing have unparalleled advantages in photoelectric conversion. Optoelectronic devices based on QDs of different composition have made great progress. However, there is still a lack of reviews on comparing the optoelectronic application level of semiconductor quantum dots (SQDs), perovskite quantum dots (PQDs), and carbon quantum dots (CDs), and also rarely providing a comprehensive summary of photocatalysis. First, this review almost completely summarizes advantages and disadvantages of common synthesis methods for QDs, especially pointing out the importance of optimization strategies for preparing high‐quality QDs such as ligand engineering, ion exchange, and purification separation. Then, compositions of SQD, PQD, CD, and corresponding optoelectronic properties are introduced, respectively. Next, the strengths and weaknesses of the four QDs structures are compared in detail. Finally, the flourishing development in various optoelectronic applications is separately demonstrated, and development degree of SQDs, PQDs, and CDs is compared to discover the most suitable application scenarios for each. On these bases, bottlenecks and opportunities are also put forwarded, hoping to stimulate more breakthroughs in this field.

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Insulator-to-semiconductor conversion of solution-processed ultra-wide bandgap amorphous gallium oxide via hydrogen annealing

Cited by 8 publications

References 27 publications

31‐2: Student Paper: Fermi Level Prediction of Solution‐processed Ultra‐wide Band gap a‐Ga₂O_x via Supervised Machine Learning Models

31‐2: Student Paper: Fermi Level Prediction of Solution‐processed Ultra‐wide Band gap a‐Ga₂O_x via Supervised Machine Learning Models

Machine-Learned Fermi Level Prediction of Solution-Processed Ultrawide-Bandgap Amorphous Gallium Oxide (a-Ga₂O_x)

Colloidal Quantum Dots: Synthesis, Composition, Structure, and Emerging Optoelectronic Applications

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Insulator-to-semiconductor conversion of solution-processed ultra-wide bandgap amorphous gallium oxide via hydrogen annealing

Cited by 8 publications

References 27 publications

31‐2: Student Paper: Fermi Level Prediction of Solution‐processed Ultra‐wide Band gap a‐Ga2Ox via Supervised Machine Learning Models

31‐2: Student Paper: Fermi Level Prediction of Solution‐processed Ultra‐wide Band gap a‐Ga2Ox via Supervised Machine Learning Models

Machine-Learned Fermi Level Prediction of Solution-Processed Ultrawide-Bandgap Amorphous Gallium Oxide (a-Ga2Ox)

Colloidal Quantum Dots: Synthesis, Composition, Structure, and Emerging Optoelectronic Applications

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31‐2: Student Paper: Fermi Level Prediction of Solution‐processed Ultra‐wide Band gap a‐Ga₂O_x via Supervised Machine Learning Models

31‐2: Student Paper: Fermi Level Prediction of Solution‐processed Ultra‐wide Band gap a‐Ga₂O_x via Supervised Machine Learning Models

Machine-Learned Fermi Level Prediction of Solution-Processed Ultrawide-Bandgap Amorphous Gallium Oxide (a-Ga₂O_x)