Development and Experimental Evaluation of a Novel Piezoresistive MEMS Strain Sensor
Bibliographic record
Abstract
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.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".