Review of plasma etching processes for III-V semiconductors
Bibliographic record
Abstract
This paper provides a comprehensive literature review and analysis of III-V semiconductor plasma etching, highlighting key etching considerations and providing literature references to inform future process development. Plasma etching processes for III-V materials such as gallium arsenide (GaAs), indium phosphide (InP), and gallium nitride (GaN) are essential to fabricate many photonic and optoelectronic devices. Such applications frequently require etches with high anisotropy, selectivity and aspect ratio while maintaining minimal roughness and lateral etching. Ten plasma etching techniques used for III-V materials as well as the impact of plasma process parameters on etch results are reviewed. Main etching challenges include aluminum oxidation, non-volatile indium etch subproducts when < 150 °C, and strong III-N bonds. Exhaustive reference tables are generated to report capacitively coupled plasma (CCP) and inductively coupled plasma (ICP) etching process parameters for the main binary, ternary, and quaternary III-V semiconductors. An analysis of binary III-V etching is presented in a summative reference plot, which highlights trends in etch rate, etch technology, and active gas chemistry. Among studies reporting etch rates, gallium arsenide was etched most frequently, with ICP being the dominant etch technique. Multilayer systems and plasma damage are briefly discussed, with post-etch treatments and hydrogen plasmas being used for damage passivation. Overall, III-V materials can be etched with plasma up to several μ m/min, with most processes using chlorine-based chemistries such as Cl 2 , BCl 3 , and SiCl 4 .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".