Single Dopant Lithography for the Fabrication of Atomic-scale Devices and Quantum Systems
Bibliographic record
Abstract
We describe approaches for the fabrication of single atom electronic devices and of devices with spin-based qubits for quantum computing. Single ion implantation is one of the main candidates for fabricating atomic-scale devices. Here, the ability to precisely locate single dopant atoms has the potential to enable deterministic, tunable control over key electronic and magneto-optical properties of the basic devices necessary for solid-state quantum computing. Further, the electrical operation of a CMOS compatible single dopant atom device operating at room-temperature (RT) is presented. It is based on a single dopant atom quantum dot (QD) transistor using phosphorous atoms isolated within nanoscale SiO2tunnel barriers. In contrast to single dopant transistors in silicon, where the QD potential well is shallow and device operation limited to ~1 K temperatures, here, a deep (~1eV) potential well allows electron confinement even at RT [1]. This suggests higher temperature operation, at ~10 K or greater if needed, for quantum electronic circuits. The transistors, based on ~10 nm size scale Si/SiO2/Si point-contact tunnel junctions defined by scanning probe lithography and ‘geometric oxidation’ of the point-contact region, have enabled the control of single [1], and double tunnel coupled QDs [2], and investigations of single particle thermodynamics in a Maxwell ‘demon’ configuration [3].
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".