Preface: SiliconPV 2023, the 13th International Conference on Crystalline Silicon Photovoltaics
Bibliographic record
Abstract
The development of PV technology is going fast and much faster than one could dream of a decade ago, and efficiency is one of the key parameters to drive down LCoE. Crystalline silicon PV is often mentioned as the workhorse for large-scale deployment of PV. Actually, to my opinion it is more than a so-called workhorse: it is the core technology, and for at least the coming decade PV technology will be based on crystalline silicon. Currently, large manufacturers are converting their production lines from PERC+ to TOPCon enabling conversion efficiencies for solar cells beyond 26%. It has been demonstrated that these efficiencies are feasible in an industrial environment. More development is needed to obtain these efficiencies in high-volume manufacturing, which is a matter of time. Furthermore, many companies are investing in silicon heterojunction technology, another silicon PV technology with which efficiencies beyond 26% are possible. The current world record silicon solar cell with an efficiency of 26.8% is a heterojunction solar cell [1]. To make the next steps and reach efficiencies beyond the ones that are theoretically possible with single junction silicon PV we need tandem technology and especially the one of hybrid perovskite/silicon tandems; a concept containing silicon PV technology as bottom device. At the time of the conference the world record for a hybrid perovskite/silicon tandem cell was 32.5% [2]. Only 1.5 month after concluding the conference the world record has already been improved by more than 1% absolute to 33.7% [3]!! With these ultra-high efficiencies solar cells will become sensitive to already a low concentration of imperfections. A deeper understanding of, for example, degradation mechanisms, interface and surface passivation, and the behavior and properties of innovative module materials to guarantee long lifetimes are key. Process technologies, advanced characterization techniques and material properties to better understand the physics and chemistry behind these aspects were presented and discussed during this inspiring conference. It was a great pleasure to meet with close to 300 scientists and engineers from all over the world during the conference that was held at the campus of Delft University of Technology (TU Delft). Many PV experts are acknowledged for reviewing abstracts and full manuscripts with this proceedings as a result. We hope that you get new inspiration by studying the proceedings of SiliconPV 2023 and will develop novel technologies that contribute to a fast energy transition. A better world will start with cheap and clean energy for everyone.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.231 | 0.163 |
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".