Molecular Passivation of Mercury−Silicon (p-type) Diode Junctions: Alkylation, Oxidation, and Alkylsilation
Bibliographic record
Abstract
To evaluate the electrical performance of molecularly modified metal−semiconductor diode junctions, organic monolayers were grafted on both hydrogen-terminated and oxidized silicon (p-type) surfaces. Three model systems, i.e., Hg|C 12 H 25 −Si, Hg|SiO 2 −Si, and Hg|C 12 H 25 SiO 3 −SiO 2 −Si, were prepared and systematically characterized based on their current−voltage and capacitance−voltage properties. The experimental results showed that mercury−silicon junctions modified with n -dodecyl monolayer display better rectifying behavior, i.e., larger rectifying ratio and smaller empirical ideality factor (i.e., close to unity), than those passivated with SiO 2 thin films and n -dodecylsiloxane monolayers (formed on oxidized silicon). The differential capacitance measurements revealed that organic modified junctions (both alkylated and alkylsilated samples) have substantially lower densities of interface states in comparison with that of Hg|SiO 2 −Si. This work provides a clear assessment of the varied device performance among differently prepared metal−molecule−semiconductor junctions, which is complementary to the topic studies on the electron transport across these molecular interfaces. More importantly, the present research augments the potential applications of molecular modification and surface engineering in the fabrication of silicon-based microelectronic devices.
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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 | 0.000 |
| 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) | 0.001 | 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".