The electron transport within the wide energy gap compound semiconductors gallium nitride and zinc oxide
Bibliographic record
Abstract
In this thesis, the electron transport that occurs within two wide energy gap semiconductors, gallium nitride and zinc oxide, is considered. Electron transport within gallium arsenide is also examined, albeit primarily for benchmarking purposes. The over-arching goal of this thesis is to provide the materials community with tools for analysis and optimization to be used when evaluating the consequences of transient electron transport within these compound semiconductors. Providing fresh insights into the character of the electron transport within zinc oxide, with particular focus on the device implications, is another aim of this analysis. Initially, Monte Carlo electron transport simulation results are used for a comparative analysis of the transient electron transport that occurs within bulk zinc-blende gallium arsenide and bulk wurtzite gallium nitride. It is found that for both materials the electron drift velocity and the average electron energy field-dependent "settling times" are strongly correlated and that the electric field resulting in the shortest electron transit-time is a function of channel length. Then, the applicability of the semi-analytical approach of Shur in evaluating the transient electron transport response within gallium arsenide, gallium nitride, and zinc oxide is critically examined. In particular, a comparison with Monte Carlo results is performed in order to establish the utility of this approach as a tool in studying the transient electron transport response. Next, a Monte Carlo analysis of the electron transport within bulk wurtzite zinc oxide is performed. The applied electric field strength that ensures the minimum electron time-to-transit across a given channel length is determined. These results are then used in order to provide an upper bound on the potential performance of zinc oxide based devices. Finally, the utility of the semi-analytical approach of Shur, for the purposes of device design optimization, is considered for the specific case of bulk wurtzite ZnO. It is found that the results produced through the semi-analytical approach of Shur are, in many cases, imperceptibly different from those of the Monte Carlo simulations. This adds to the allure of the semi-analytical approach as a versatile tool for transient electron transport analyzes and 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 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 | 0.001 | 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 | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".