Toward a GHz-Frequency BEOL Ferroelectric Negative-Capacitance Oscillator With a Wide Tuning Range
Bibliographic record
Abstract
The potential to utilize negative-capacitance dynamics in a ferroelectric capacitor as a back-end-of-line (BEOL) element to construct a tuned oscillator operating in the GHz range is proposed and investigated. Using tools established in the field of non-linear dynamics, the operating principles of the circuit are rigorously explored, a criterion for oscillation is developed, and amplitude and frequency control are investigated. Furthermore, this novel architecture is compared with a traditional LC oscillator. Through the comparison, we find that the FE oscillator can provide a substantially larger tuning range (149%, between 1.29 GHz–8.75 GHz, vs. 50% achieved by the traditional LC oscillator) and requires a vastly lower on-chip area <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\sim\!\! 50\,{\bm{\mu}}{{\mathbf{m}}^2}\,\text{vs}{\rm{.}}\,\sim 40000\,{\bm{\mu}}{{\mathbf{m}}^2})$</tex-math></inline-formula>, while achieving a similar figure of merit <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{FO}{{\mathbf{M}}_2}$</tex-math></inline-formula> (reduced by only 6 dB). Such improvements motivate the continued exploration and development of negative-capacitance ferroelectrics as BEOL elements that can significantly improve integrated-circuit performance.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".