Modeling of Hysteretic Jump Points in Ferroelectric MOS Capacitors
Bibliographic record
Abstract
Negative capacitance devices generally exhibit hysteresis, which can be exploited for memory but should be suppressed for logic devices. The significant nonlinearity of ferroelectric (FE) metal–oxide–semiconductor (MOS) capacitor makes it difficult to manipulate hysteresis, especially when multiple material and device parameters are considered simultaneously. Here we model hysteretic jump points (HJPs) and describe how hysteresis responds to different parameters used. First, the energy landscape of FE-MOS capacitor is explored based on the Gibbs free energy, considering forward and backward sweep of gate voltage, to identify the HJPs. Our simulation shows that the surface potential of HJP has a logarithmic relation to the doping concentration of the semiconductor while FE thickness (${T}_{\text {FE}}$) and gate oxide thickness (EOT) shift the value of the surface potential up or down. Based on the developed model, we introduce hysteresis width and height to evaluate the extent of hysteresis and the amplification of potential quantitatively. Our results show that hysteresis width is a strong function of EOT and${T}_{\text {FE}}$but the potential amplification is limited, especially when EOT is thin. In addition, the effect of doping concentration on the hysteresis window is minimal, particularly with thick FE layer. Our model provides a useful tool to directly investigate hysteresis, which makes it possible to modify the hysteresis window by engineering parameters for different target applications.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".