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Record W2124488572 · doi:10.1109/jsen.2011.2113374

Development and Experimental Evaluation of a Novel Piezoresistive MEMS Strain Sensor

2011· article· en· W2124488572 on OpenAlexaff
Ahmed Mohammed, Walied A. Moussa, Edmond Lou

Bibliographic record

VenueIEEE Sensors Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPiezoresistive effectSensitivity (control systems)Materials scienceDopingTemperature coefficientSiliconAnalytical Chemistry (journal)OptoelectronicsElectronic engineeringComposite materialChemistryEngineering

Abstract

fetched live from OpenAlex

This paper presents the experimental evaluation of a new piezoresistive microelectromechanical systems strain sensor. The sensing chip is highly capable of measuring biaxial state of strain/stress. The sensing elements are p-type piezoresistors on (100) single crystal silicon aligned along [110] and its in-plane transverse. The concept of introducing geometric features to enhance the sensor sensitivity is investigated. The results of experimental evaluation and finite-element analysis (FEA) proved the viability of this concept to improve the sensor sensitivity. The microfabrication process utilizes five doping concentrations to explore the effect of doping level on the sensor performance. The sensor is developed considering applications under varying temperature conditions. Therefore, high doping concentration (more than 1 ×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19</sup> atoms/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) is favorable to reduce the sensor thermal drift. As a result, the sensor sensitivity is significantly reduced. Hence, geometric features are introduced in the sensor silicon carrier to compensate for the signal loss through stress concentration effect, which magnified the strain field in the proximity of the sensing elements. In addition, the use of full-bridge configuration reduced the overall temperature coefficient of resistance (TCR). At doping concentration of ~5 ×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19</sup> atoms/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , the measured strain sensitivity is 0.035 mV/με for input voltage of 5 volts, which corresponds to an effective gauge factor of ~7 and piezoresistive gauge factor of ~44. The effective gauge factor includes all the signal losses and the effect of bonding adhesive. Design and analysis, prototyping, and experimental evaluation are presented. Finally, guidelines to select the bonding adhesive and packaging scheme are provided.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

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

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 head0.079
GPT teacher head0.285
Teacher spread0.206 · 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 teacher head, 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

Citations25
Published2011
Admission routes1
Has abstractyes

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