Radio Frequency Reflectometry of Single-Electron Box Arrays for Nanoscale Voltage Sensing Applications
Bibliographic record
Abstract
Single-electron tunneling transistors (SETs) and boxes (SEBs) exploit the phenomenon of Coulomb blockade to achieve unprecedented charge sensitivities. Single-electron boxes, however, despite their simplicity compared to SETs, have rarely been used for practical applications. The main reason for that is that unlike a SET where the gate voltage controls conductance between the source and the drain, an SEB is a two terminal device that requires either an integrated SET amplifier or high-frequency probing of its complex admittance by means of radio frequency reflectometry (RFR). The signal to noise ratio (SNR) for a SEB is small, due to its much lower admittance compared to a SET and thus matching networks are required for efficient coupling ofSEBs to an RFR setup. To boost the signal strength by a factor of N (due to a random offset charge) SEBs can be connected in parallel to form arrays sharing common gates and sources. The smaller the size of the SEB, the larger the charging energy of a SEB enabling higher operation temperature, and using devices with a small footprint (<0.01 µm2), a large number of devices (>1000) can be assembled into an array occupying just a few square microns. We show that it is possible to design SEB arrays that may compete with an SET in terms of sensitivity. In this, we tested SETs using RF reflectometry in a configuration with no DC through path (“DC-decoupled SET” or DCD SET) along with SEBs connected to the same matching network. The experiment shows that the lack of a path for a DC current makes SEBs and DCD SETs highly electrostatic discharge (ESD) tolerant, a very desirable feature for applications. We perform a detailed analysis of experimental data on SEB arrays of various sizes and compare it with simulations to devise several ways for practical applications of SEB arrays and DCD SETs.
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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".