(Invited) Homogeneous and Heterogeneous Material Based Nanotube Tunnel Field Effect Transistor with Core-Shell Gate Stacks
Bibliographic record
Abstract
In the last forty years, complementary metal oxide semiconductor (CMOS) technology has made tremendous progress and its advancement has enabled physical scaling of CMOS electronics technology. Physical scaling has offered us higher data processing performance but with the increased penalty of power consumption. As we approach physical scaling, alternative materials, device architecture, physics and integration strategies have been proposed to overcome this fundamental physical roadblock. This problem specially exaggerates for implantable and bioelectronics where we need higher data performance but at the same time lower power consumption. Specially body integration restricts heat dissipation related power consumption to 40 mW/cm2. Therefore, we have conceptualized a newly minted nanotube architecture with the classical crystalline material such as silicon (Si), Silicon-Germanium (SiGe), Germanium (Ge), III-V materials and applying tunnel physics we have developed an integration strategy where a core-shell gate stacks in a coin like configuration (sensors on sensing surface connected via through-polymer-via (TPV) to homo and heterogeneous crystalline materials based nanotube tunnel FETs with core-shell gate stacks on the other side for higher information processing performance and lower power consumption. In this talk, we will discuss this novel device architecture, its physics, choice of materials and integration strategy specially for brain-machine interfacing.
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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 | 0.000 |
| 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) | 0.005 | 0.002 |
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".