Electrical and Microstructural Analysis of Contact Formation on Lightly Doped Phosphorus Emitters Using Thick-Film Ag Screen Printing Pastes
Bibliographic record
Abstract
Screen printing of the metallization of phosphorus diffused emitters is a well-established process for industrial silicon wafer-based solar cells. Previously, screen printed silver pastes typically required a very high phosphorus surface doping concentration to ensure a low-resistance ohmic contact. Recently, paste manufacturers have focused on the development of silver pastes capable of contacting phosphorus emitters with progressively lower surface concentrations, to minimize surface recombination losses and enable higher cell conversion efficiencies. In this paper, we report on the progress of contacting inline-diffused phosphorus emitters, of which the surface concentrations have been reduced by an etch-back process, using two different pastes. Solar cells with emitter surface concentrations ranging from 4.0 × 1020to 1.7 × 1020phosphorus atoms/cm3were made using two different silver pastes. We present a microstructural analysis of the contact formation, which indicates the possible dominant current transport mechanisms for the two pastes. A high density of silver crystallites formed with a very narrow interfacial glass layer makes the Sol 9600 paste suitable for contacting lowly doped phosphorus emitters. Efficiency gains of 0.2%-0.3% (absolute) were achieved, reaching a maximum efficiency of 18.6% on 156 mm × 156 mm p-type pseudo-square Cz mono-crystalline silicon solar cells.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".