Piezo-VFETs: Vacuum Field Emission Transistors Controlled by Piezoelectric MEMS Sensors as an Artificial Mechanoreceptor with High Sensitivity and Low Power Consumption
Bibliographic record
Abstract
As one of the most promising electronic devices in the post-Moore era, nanoscale vacuum field emission transistors (VFETs) have garnered significant attention due to their unique electron transport mechanism featuring ballistic transport within vacuum channels. Existing research on these nanoscale vacuum channel devices has primarily focused on structural design for logic circuits. Studies exploring their application potential in other vital fields, such as sensors based on VFET, are more limited. In this study, for the first time, the design of a vacuum field emission transistor (VFET) coupled with a piezoelectric microelectromechanical (MEMS) sensing unit is proposed as the artificial mechanoreceptor for sensing purposes. With a negative threshold voltage similar to an N-channel depletion-mode metal oxide silicon field effect transistor, the proposed VFET has its continuous current tuned by the piezoelectric potential generated by the sensing unit, amplifying the magnitude of signals resulting from electromechanical coupling. Simulations have been conducted to validate the feasibility of such a configuration. As indictable from the simulation results, the proposed piezoelectric VFET exhibits high sensitivity and an electrically adjustable measurement range. Compared to the traditional combination of piezoelectric MEMS sensors and solid-state field effect transistors (FETs), the piezoelectric VFET design has a significantly reduced power consumption thanks to its continuous current that is orders of magnitude smaller. These findings reveal the immense potential of piezoelectric VFET in sensing applications, building up the basis for using VFETs for simple, effective, and low-power pre-amplification of piezoelectric MEMS sensors and broadening the application scope of VFET in general.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".