MétaCan
Menu
Back to cohort
Record W2157864236 · doi:10.1049/mnl.2011.0470

Temperature compensation in integrated CMOS-MEMS scanning probe microscopes

2012· article· en· W2157864236 on OpenAlexaff
Niladri Sarkar, Kyle Trainor, Raafat R. Mansour

Bibliographic record

VenueMicro & Nano Letters · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPiezoresistive effectMicroelectromechanical systemsActuatorChipScannerMaterials scienceComputer scienceElectronic engineeringEngineeringElectrical engineeringNanotechnologyOptoelectronicsArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.249
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueMicro & Nano LettersSame topicForce Microscopy Techniques and ApplicationsFrench-language works237,207