Photoresponse of MoSe<sub>2</sub> Transistors: A Fully Numerical Quantum Transport Simulation Study
Bibliographic record
Abstract
Phototransistors made with two-dimensional transition metal dichalcogenides (TMDs) have shown excellent potential for nanoscale optoelectronic applications. In this work, we perform fully numerical simulations to investigate photoresponse mechanisms in MoSe 2 transistors, considering photoconductive and photogating (PG) effects. Our model implements PG by self-consistently solving a trapped charge distribution with electrostatics and transport in the channel. The results are in good agreement with the reported experimental device characteristics and explain the PG effect by quantifying potential barrier lowering and trapped carrier concentration upon illumination. We study the two mechanisms in isolation and reveal the dominance of the PG effect on the photocurrent at high gate voltages. Additionally, we show a trade-off between photoresponsivity and specific detectivity at different gate voltages and find that the gain of the phototransistor decreases with increased optical power density due to the saturation of trapped carriers. Finally, we show that photoresponsivity can be tuned over several orders of magnitude by varying trap-state energy, capture cross sections, total concentration of trap states, and recombination lifetime, all of which can be changed through material optimization. Our work highlights the underlying physics of photoresponse in TMD devices and presents a model which can be used for future device design.
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".