Temperature compensation in integrated CMOS-MEMS scanning probe microscopes
Bibliographic record
Abstract
The authors report on the operation of complementary metal-oxide semiconductor microelectromechanical system (MEMS) scanning probe microscope (SPM) with integrated three-dimensional electrothermal actuation and three-axis position sensing. Conventional SPM makes use of piezoelectric positioning systems which are bulky (leading to thermal drift and poor vibration immunity) and suffer from inherent creep (leading to image distortion). The scanner design is intended to leverage the myriad physical benefits of dimensional scaling to improve the performance and to reduce the barrier to entry for SPM ownership when compared to the state-of-the-art. However, the integration of multiple electrothermal actuators on chip introduces several complications owing to coupling between electrical, thermal and mechanical domains. The focus of this Letter is to discuss the origins of these effects, and the strategies that are implemented to mitigate them. Specifically, the authors discuss the open-loop and closed-loop control methods that are used to drive the lateral and vertical actuators and propose and verify a method to compensate for the parasitic effects observed in the piezoresistive force sensors. To the best of the authors' knowledge, this is the first integrated MEMS-SPM with multiple imaging modes that can image a sample without the need for off-chip scanners or laser-based position sensing.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".