Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As CMOS technology is scaled down toward the nanoscale regime, drastically growing leakage currents and variations in device characteristics are becoming two important design challenges. Traditionally, the device-design methodology is based on finding the device parameters which minimize the leakage current while providing a minimum saturation current for the transistor. This methodology may change when variations are accounted for design. In this paper, a novel device optimization methodology is presented that incorporates variability awareness into the device-design flow such that the designed device satisfies desired bounds on total leakage, saturation current, and intrinsic delay under parameter variabilities. The technique locates the maximum-yield rectangular cube in the 5-D feasible space composed of oxide-thickness, gate-length, and channel-doping profile parameters. The center of this cube is considered as the maximum-yield design point with the highest immunity against variations. By using the methodology, four high-performance (HP) and low-power devices in 90-nm technology and one HP device in 65 nm have been designed. Monte Carlo simulations have been done to investigate the devices' performance and power metric variations and to verify their yield maximality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it