Device model for intermediate band materials
Bibliographic record
Abstract
For twenty years, intermediate band (IB) materials have been developed with the goal of making high-efficiency photovoltaics, with limiting efficiencies equivalent to triple-junction devices but with simpler and potentially less expensive device designs. IB devices have yet to produce any high efficiencies. Existing devices did not optimize such parameters as their layer thicknesses, because there was no device model that could treat all the IB-specific effects, e.g., charge transport within the IB and IB-filling-dependent absorptivity and photofilling. We present Simudo, a finite element optoelectronic device model that implements these effects, in addition to treating standard semiconductors. Simudo models charge transport and generation in the conduction, valence, and a number of intermediate bands. It solves the coupled Poisson/drift-diffusion equations in two dimensions, along with self-consistent optics for IB-filling-dependent absorption. We validate this new software by benchmarking it against Synopsys Sentaurus on a pn-diode test problem, and we show excellent agreement. Simudo enables optimization of devices as well as understanding of experimental results, bringing the well-established value of device modeling semiconductors to IB systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".