From Amorphous to β-Gallium Oxide: Practical Implementation of Energetics Considerations in Process Design and Optimization
Bibliographic record
Abstract
Gallium oxide (Ga2O3) is a wide bandgap material (bandgap ~4.0 eV – 5.2 eV) with a large breakdown field that has considerably high figures of merit (FOM) in power handling compared to other wide bandgap semiconductor materials in use today (such as GaN and SiC) [1, 2]. Gallium oxide is also expected to expand the operating spectral range of optoelectronic devices to deep UV. Properties of gallium oxide depend on its crystal structure; amorphous [3, 4] as well as different crystalline forms [5] of this material have been used in electronic and optoelectronic devices. Among gallium oxide crystalline polymorphs, β-Ga2O3 has attracted the most attention because it is the most stable gallium oxide polymorph and, therefore, can ultimately be obtained by heating other gallium oxide polymorphs (and even amorphous gallium oxide) at sufficiently high temperatures (ca. 550°C and above); this polymorph can also be obtained from the melt at high temperatures (ca. 1800°C) using bulk crystal growth techniques [1, 6]. In the thin film form, growing high quality β-Ga2O3 is only possible on very limited substrates (e.g., β-Ga2O3 native substrate and sapphire) while having to carefully choose very specific process conditions based on each process and the instrument being used. In this work, we present strategies and guidelines, based on energetics considerations, that make it possible to design epitaxial deposition processes that achieve β-Ga2O3 thin films at low temperatures (< 300°C). We use the atomic layer deposition (ALD) technique to achieve dense and pinhole-free films of amorphous gallium oxide. Then, we revise the deposition process conditions step-by-step so that the energetics of the process can lead us to obtain high quality epitaxial β-Ga2O3 at low temperatures while not being limited to β-Ga2O3 native substrates or very specific (or instrument-dependent) process conditions. The results presented in this work facilitate the implementation of Ga2O3 in next generation wide bandgap electronic devices. References: [1] Pearton, S. J.; Yang, J.; Cary, P. H.; Ren, F.; Kim, J.; Tadjer, M. J.; Mastro, M. A. A Review of Ga2O3 Materials, Processing, and Devices. Appl. Phys. Rev. 2018 , 5, 011301. [2] Rafie Borujeny, E.; Sendetskyi, O.; Fleischauer, M. D.; Cadien, K. C. Low Thermal Budget Heteroepitaxial Gallium Oxide Thin Films Enabled by Atomic Layer Deposition. ACS Appl. Mater. Interfaces 2020 , 12, 44225-44237. [3] Kim, J.; Sekiya, T.; Miyokawa, N.; Watanabe, N.; Kimoto, K.; Ide, K.; Toda, Y.; Ueda, S.; Ohashi, N.; Hiramatsu, H.; Hosono, H.; Kamiya, T. Conversion of an Ultra-Wide Bandgap Amorphous Oxide Insulator to a Semiconductor. NPG Asia Mater. 2017, 9, e359. [4] Xiao, S.; Deng, Y.; Chen, Z.; Wang, Y.; Yu, J.; Tang, W.; Wu, Z. Flexible and Highly Stable Solar-Blind Photodetector Based on Room-Temperature Synthesis of Amorphous Ga2O3 Film. J. Phys. D: Appl. Phys. 2020, 53, 484004. [5] Ahmadi, E.; Oshima, Y. Materials Issues and Devices of α- and β-Ga2O3. J. Appl. Phys. 2019 , 126, 160901. [6] Mastro, M. A.; Kuramata, A.; Calkins, J.; Kim, J.; Ren, F.; Pearton, S. J. Perspective—Opportunities and Future Directions for Ga2O3. ECS J. Solid State Sci. Technol. 2017 , 6, P356-P359.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".