MEMS mechanical logic units: characterization and improvements of devices fabricated with MicraGEM and PolyMUMPs
Bibliographic record
Abstract
We are currently developing basic building blocks for creating digital logic units that are based on mechanical components. Transistors, which are semiconductor devices, rely on doping to change intrinsic semiconductor to extrinsic semiconductors. However, at low or high temperatures, that control is impossible as semiconductors revert to intrinsic behaviour. Also, semiconductors exhibit various complications under ionizing (radiation) environment. We have fabricated logic units using micro-mechanical relays using MEMS technology. The logic units consist of a micro-mechanical relay with three electrical gates. The mechanical relay is fabricated with a cantilever over an airgap, and is operated by applying voltage to the gate. The applied voltage creates an electric force between the gate and a cantilever structure. The electrostatic force arches the cantilever into electrical contact. Since the operation does not depend on controlling the type of charge carriers, the proposed method does not suffer from the limitations shared by semiconductors. With different input combinations applied to the gates of the device, development of MEMS mechanical logic, leading to general digital circuits, is possible. Characterization of the devices is performed, which includes operation times, operation voltages, and maximum currents. Design, fabrication and testing of these micro-mechanical logic elements will be presented in the paper.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".