Advances in etching of 2D nanomaterials: Research challenges and advanced devices
Bibliographic record
Abstract
Etching is central to the processing of two-dimensional (2D) materials, providing atomic-level precision needed to tailor their structural, electronic, and optical properties. Despite advances in plasma, chemical, and atomic layer etching, major challenges remain in achieving reliable depth control, defect management, and anisotropy at scales compatible with industrial manufacturing. The intrinsic sensitivity of 2D materials to processing conditions, coupled with substrate interactions, often limits reproducibility and device performance. Future progress will depend on methods that unite throughput with atomic precision, including resist-free and direct-write approaches that bypass conventional lithography, selective chemistries for multi-material heterostructures, and artificial intelligence–driven process control for real-time optimization. Advances in substrate engineering and interfacial selectivity will also be pivotal for wafer-scale integration. By defining key barriers and highlighting emerging opportunities, this review identifies the strategies most likely to transform 2D etching into a scalable platform for electronics, photonics, quantum technologies, and energy devices.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".