Facile Grown Native Oxide Based Passivation of Crystalline Silicon: A Novel Approach for Low-Temperature Synthesis of Silicon Photovoltaics
Bibliographic record
Abstract
Passivation of the crystalline silicon surface is central to the attainment of high-efficiency silicon solar cells and even more so as the silicon absorber is thinned. Moreover, the current predominantly high temperature processing of thin silicon wafers gives rise to defect migration-creation and thermal stresses in a typical multi-layered photovoltaic (PV) device. The development of a simple and effective low temperature passivation scheme would aid immensely in the synthesis of next generation low-cost high-efficiency thin silicon solar cells. \n \nThis research proposes, investigates and demonstrates the efficacy of a novel low-temperature passivation scheme for crystalline silicon surface which consists of a facile grown native oxide (SiOx) layer and a silicon nitride (SiNx) over layer. A systematic experimental study of the interfacial passivation quality reveals that high quality surface passivation is obtained at a saturation native oxide thickness of ~10Å. The passivation quality is uniform over a large silicon surface with a surface recombination velocity (SRV) of 8 cm/s. Recombination modelling of the interface shows that the interfacial defect density diminishes with increasing native oxide thickness while the trapped charge density is essentially unchanged. \n \nIn light of the new passivation scheme, this research investigates theoretically and experimentally a legacy photovoltaic cell concept, the Back Amorphous-Crystalline silicon Heterojunction (BACH) PV device, and proposes a novel PV cell concept - the Lateral Inherently Thin (LIT) amorphous-crystalline silicon heterojunction PV device. \n \nBenchmarked theoretical studies of the devices, using Sentaurus - a device modelling code, indicate a maximum BACH cell and LIT cell efficiency of 24.4% and 23.9%, respectively, for a 100µm thick textured cSi substrate with attainable (10 cm/sec) passivation quality. \nBACH and LIT PV devices, integrating the new facile grown SiOx passivation scheme, were fabricated using n-type double-side polished 280µm thick (100) FZ cSi wafers. An optimal untextured cell efficiency of 16.7% and 11.6% are obtained for a 1 cm2 BACH and LIT devices, respectively, under AM1.5 solar irradiation. \nDevelopment of the high quality facile grown oxide based passivation scheme, a paradigm shift relative to conventional thermal oxide passivation, paves the path for low-temperature synthesis of oxide-based high quality semiconductor devices on thin silicon - including high-efficiency silicon photovoltaics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 teacher head, 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".