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Record W4242750331 · doi:10.1149/ma2014-01/39/1439

(Invited) Spatial ALD, Deposition of Al<sub>2</sub>O<sub>3</sub> Films at Throughputs Exceeding 3000 Wafers per Hour

2014· article· en· W4242750331 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsCégep de Lévis
Fundersnot available
KeywordsPassivationAtomic layer depositionWaferDangling bondDielectricMaterials scienceSemiconductorOptoelectronicsNanotechnologySemiconductor device fabricationThin filmEngineering physicsLayer (electronics)SiliconPhysics

Abstract

fetched live from OpenAlex

During the last few years various types of ALD films have successfully been introduced in high-volume manufacturing in a number of industries. Particularly in the semiconductor industry the implementation is wide spread (high-k gate stacks, capacitor dielectrics, diffusion barriers, etc.). More recently a specific type of ALD film, Al 2 O 3 , was widely researched as a potential surface passivation layer for solar cell surfaces in the PV industry. Thin (&lt; 10nm) Al 2 O 3 films passivate p-type surfaces very effectively. On the one hand intrinsic negative charges in the dielectric film repel charge carriers, while on the other hand hydrogen that is present in the ALD films passivates dangling bonds at the Si/SiO 2 / Al 2 O 3 interface. The combination of both effects reduces the charge recombination losses considerably. The overall result is an increase in cell efficiency of 0.5-1% (absolute). Based on extensive testing of these films, it is to be expected that Implementation in PV manufacturing will take place in the near future. There is a large difference in film and process requirements of ALD films applied in the semiconductor and PV industries. Whereas uniformity and defects (particles) are very important in the IC industry this is much less so in PV. Much more important in the PV industry are cost and throughput. Typical numbers for both industries are a Cost of Ownership of 2-10 vs. 0.02-0.05$/wafer, respectively, and 10-50 vs 1500-2500 wfrs/hr.. As ALD processes are notoriously slow, ways must be found to increase the throughputs considerably. This is done by either using batch furnaces loaded with 500-1000 wafers per batch, or in-line systems in which wafers are loaded, processed and unloaded in a continuous flow of 0.5-0.8 wafers/s. The latter type of system is based on the so-called spatial ALD process. An example is shown in the figure. In this system (Levitrack) wafers are loaded in a track in which zones with precursors TMA and H 2 O are spatially separated by zones of inert gas. While floating on a gas cushion, the wafers pass ALD ‘cells’ with fixed sequences of TMA and water, adding a thin layer of Al 2 O 3 with each passage through a cell. The advantages of this approach are several: high throughput, atmospheric processing, no deposition on the inside walls of the track, no moving parts such as valves, pumps, etc. Typical results obtained with this system are: film uniformity 3-4%, life time of solar cell charge carriers &gt; 3ms, efficiency enhancement 0.4-0.8%, CoO ~ 0.04$/wfr. In the paper more details will be provided on the economy of this type of system, as well as on film characteristics and efficiency enhancements realized in a variety of solar cell structures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.214
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it