Self-Aligned van der Waals Heterojunction Diodes and Transistors
Bibliographic record
Abstract
A general self-aligned fabrication scheme is reported here for a diverse class of electronic devices based on van der Waals materials and heterojunctions. In particular, self-alignment enables the fabrication of source-gated transistors in monolayer MoS 2 with near-ideal current saturation characteristics and channel lengths down to 135 nm. Furthermore, self-alignment of van der Waals p–n heterojunction diodes achieves complete electrostatic control of both the p-type and n-type constituent semiconductors in a dual-gated geometry, resulting in gate-tunable mean and variance of antiambipolar Gaussian characteristics. Through finite-element device simulations, the operating principles of source-gated transistors and dual-gated antiambipolar devices are elucidated, thus providing design rules for additional devices that employ self-aligned geometries. For example, the versatility of this scheme is demonstrated via contact-doped MoS 2 homojunction diodes and mixed-dimensional heterojunctions based on organic semiconductors. The scalability of this approach is also shown by fabricating self-aligned short-channel transistors with subdiffraction channel lengths in the range of 150–800 nm using photolithography on large-area MoS 2 films grown by chemical vapor deposition. Overall, this self-aligned fabrication method represents an important step toward the scalable integration of van der Waals heterojunction devices into more sophisticated circuits and 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".