Flexible touchpads based on inductive sensors using embedded conductive composite polymer
Bibliographic record
Abstract
We present the design, fabrication, and preliminary testing of a flexible array of sensor switches intended for applications in wearable electronics and sensor systems. The touch pad sensor arrays feature flexible printed circuit board (flexible PCB) substrates and/or flexible conductive composite polymer (CCP) structures, resulting in highly flexible switch arrays. Each switch consists of 4 elements: fascia, target, spacer and a sensor coil. The user presses the fascia, bringing the target in contact with the sensor coil. Any change in the position of the target changes the coil inductance due to the generation of eddy currents, which are detected by an electronic circuit and custom software. Contact between the target and coil also measurably changes the inductance of the coils. Different sizes and geometries (square, circular, hexagonal and octagonal) of coils in both flexible PCB metal (copper) and CCP were investigated to determine which couple best with the CCP that forms the target for the inductive coils. We describe techniques for patterning two-layer inductive coils on flexible PCBs. Using this process, we demonstrate coil trace thicknesses of 200 micrometers. We also present a new low cost microfabrication technique to create inductive flexible coils using embedded CCP in polydimethylsiloxane (PDMS) as an alternative to flexible PCB metal coils. We further describe an electronic circuit that accurately measures inductances as low as 500 nH that is used to detect the change in the inductance of a sensor’s coil when the user presses the target element of the sensor. The inductance for a sensor composed of CCP square coils and CCP target was measured to be approximately 35 μH before being pressed. When pressed, the inductance dropped to 3.8 μH, a change which was easily detected.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".