Surface State Dynamics Dictating Transport in InAs Nanowires
Bibliographic record
Abstract
Because of their high aspect ratio, nanostructures are particularly susceptible to effects from surfaces such as slow electron trapping by surface states. However, nonequilibrium trapping dynamics have been largely overlooked when considering transport in nanoelectronic devices. In this study, we demonstrate the profound influence of dynamic trapping processes on transport in InAs nanowires through an investigation of the hysteretic and time-dependent behavior of the transconductance. We observe large densities (∼10 13 cm –2 ) of slow surface traps and demonstrate the ability to control and permanently fix their occupation and charge through electrostatic manipulation by the gate potential followed by thermal deactivation by cryogenic cooling. Furthermore, we observe a transition from enhancement- to depletion-mode and a 400% change in field-effect mobility within the same device when the initial gate voltage and sweep rate are varied, revealing the severe impact of electrostatic history and dynamics on InAs nanowire field-effect transistors. A time-dependent model for nanowire transconductance based on nonequilibrium carrier population dynamics with thermally activated capture and emission was constructed and showed excellent agreement with experiments, confirming the effects to be a direct result of the dynamics of slow surface traps characterized by large thermal activation barriers (∼ 700 meV). This work reveals a clear and direct link between the electrical conductivity and the microscopic interactions of charged species with nanowire surfaces and highlights the necessity for considering dynamic properties of surface states in nanoelectronic devices.
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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) | 0.000 | 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) | 0.000 | 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".