Nanomanufacturing: High-Throughput, Cost-Effective Deposition of Atomic Scale Thin Films via Atmospheric Pressure Spatial Atomic Layer Deposition
Bibliographic record
Abstract
The demand for materials and devices with dimensions on the nanometer scale continues to increase. To meet this demand, high-throughput, cost-effective methods for depositing nanoscale thin films are needed. In the past few years, atmospheric pressure spatial atomic layer deposition (AP-SALD) has emerged as a potential nanomanufacturing method that is scalable, open air, and operates at modest temperatures that are compatible with flexible substrates. In this Perspective, we compare AP-SALD to other high-throughput techniques for depositing nanometer-scale thin films, including gravure printing, screen printing, knife-over-edge coating, slot-die coating, inkjet printing, spray deposition, as well as high-throughput sputtering and evaporation. Although AP-SALD does not provide the same patterning capabilities as some of these printing techniques, it offers multiple advantages: it produces continuous, conformal coatings with few defects; it requires minimal thermal treatment of the deposited materials; it provides atomic scale thickness control; it facilitates tuning of material properties; and no vacuum chamber is required, which simplifies maintenance requirements and minimizes the operating cost. Areas for further development are identified, which will allow these advantages to be leveraged: new precursors need to be developed to enable deposition of a wider variety of materials, precursor recycling should be examined, and AP-SALD systems that are high-throughput (roll-to-roll coating speeds of tens or hundreds of meters per minute) and low-maintenance need to be further developed and tested.
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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) | 0.000 | 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 | 0.001 |
| 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".