Review Article: Recommended reading list of early publications on atomic layer deposition—Outcome of the “Virtual Project on the History of ALD”
Bibliographic record
Abstract
Atomic layer deposition (ALD), a gas-phase thin film deposition technique based on repeated, self-terminating gas–solid reactions, has become the method of choice in semiconductor manufacturing and many other technological areas for depositing thin conformal inorganic material layers for various applications. ALD has been discovered and developed independently, at least twice, under different names: atomic layer epitaxy (ALE) and molecular layering. ALE, dating back to 1974 in Finland, has been commonly known as the origin of ALD, while work done since the 1960s in the Soviet Union under the name “molecular layering” (and sometimes other names) has remained much less known. The virtual project on the history of ALD (VPHA) is a volunteer-based effort with open participation, set up to make the early days of ALD more transparent. In VPHA, started in July 2013, the target is to list, read and comment on all early ALD academic and patent literature up to 1986. VPHA has resulted in two essays and several presentations at international conferences. This paper, based on a poster presentation at the 16th International Conference on Atomic Layer Deposition in Dublin, Ireland, 2016, presents a recommended reading list of early ALD publications, created collectively by the VPHA participants through voting. The list contains 22 publications from Finland, Japan, Soviet Union, United Kingdom, and United States. Up to now, a balanced overview regarding the early history of ALD has been missing; the current list is an attempt to remedy this deficiency.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.171 | 0.142 |
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".