(Digital Presentation) Fully Integrated Strain-Neutralized 2D Transistors
Bibliographic record
Abstract
As performant and well-established as conventional silicon-based electronics have become, the era of wearable electronics and the Internet-of-Things has created a demand for robust electronic devices that can conform to the surfaces of the human body. Whereas the mechanical mismatch between rigid silicon electronics and the human body represents a fundamental limit to conventional non-invasive health sensing, wearable electronics and electrodes that can conform to the microscopic features of the skin 1,2 can circumvent most of the motion artifacts inherent to conventional, rigid sensing devices, and facilitate continuous health monitoring as is required for modern, more proactive healthcare. Unfortunately, without addressing this fundamental mechanical incompatibility, devices that leverage the high density of transistors available in rigid silicon-based integrated circuits are handicapped by how well they can maintain contact with the body, and consequently are prone to failure at the sensor-circuit interface. The extraordinary properties of two-dimensional materials pose a unique opportunity for addressing this mechanical mismatch. Their unusual mechanical strength combined with their ultimate thinness, optical transparency, and favorable electronic transport properties 3 makes them ideal candidates for the next generation of highly conformable wearable electronics free of the constraints of a rigid silicon circuit board—however, minimizing local strain in the vicinity of the active devices to ensure reliable operation remains a priority. Using a design informed by finite element method (FEM) simulations, our proposed strain-neutralizing 2D transistors are configured to resist applied strains on the order of the 30% strains human skin can withstand by redistributing strain away from active regions. Tight binding simulations of the transistor channels helps with further compensation of residual strain in the active regions, alongside careful consideration of materials and device architecture during fabrication. Together, these considerations help realize the possibility of fully integrated strain-neutralized 2D transistors compatible with state-of-the-art conformable wearable sensors. [1]S. Kabiri Ameri et al., “Graphene electronic tattoo sensors,” ACS Nano, 11, 7634–7641, 2017. [2] S. Kabiri Ameri et al., “ Imperceptible electrooculography graphene sensor system for human–robot interface ”, npj 2D Materials and Applications, 2, 1-7, 2018. [3] A. H. Castro Neto, F. Guinea, N. M. R. Peres, K. S. Novoselov, and A. K. Geim, “The electronic properties of graphene,” Rev. Mod. Phys. , vol. 81, no. 1, pp. 109–162, Jan. 2009, doi: 10.1103/RevModPhys.81.109 .
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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".