High-performance digital control system for scanning tunnelling microscopy
Bibliographic record
Abstract
This paper describes a flexible, completely digital, scanning tunnelling microscope developed around a fixed-point (TMS320C542) digital signal processor. During the development special attention has been paid to the cost of the instrument, without limiting its performance, and in some regards enhancing it. The instrument has been developed and tested in the air, at room temperature, and atomic resolution has been achieved. Its software provides a maximum of support to the user. The tip approach is completely automated. The control parameters can be adjusted based on an on-line identification and off-line (in simulation) optimization. This technique is completely integrated to the control software. It greatly simplifies the parameter optimization, and completely eliminates the risk of collision between the tip and the sample during the optimization. The scanning of the image and control of the tunnelling current are implemented in software by the DSP. This allows the precise identification and real-time compensation of the capacitive coupling between the scan tube electrodes and the current detector. The image analysis and processing software allows slope compensation, as well as the presentation of differential image, two-dimensional FFT and three-dimensional image.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".