Rigorous analysis on the operating mechanism of gate-injection ferroelectric flash
Bibliographic record
Abstract
In this paper, the operation mechanism of gate-injection ferroelectric flash (GI-FeFlash) is studied through technology computer-aided design (TCAD) simulations. It reveals that GI-FeFlash exhibits an extraordinarily large memory window (MW) compared to the sum of MWs of a ferroelectric field-effect transistor (FeFET) and a charge trap flash (CTF), attributed to the synergistic effects between the two mechanisms: a polarization switching and a charge trapping. These synergistic effects can be analyzed by two phenomena: 1) a trap-assisted polarization switching (TAPS) and 2) a polarization-assisted charge trapping (PACT). In the case of TAPS, the trapped charges in the charge trap layer (CTL) enhance the polarization in the ferroelectric (FE) layer by increasing the electric field of the blocking oxide (i.e., FE layer). On the other hand, in the case of PACT, the polarization in the FE layer helps the charge injection into the CTL by increasing the electric field of the tunnel oxide (TO). Furthermore, TAPS and PACT are also verified by the analytical models based on Gauss’s law. Therefore, GI-FeFlash can achieve a large MW (∼14.9 V) with a low-program/erase voltage and a fast-speed operation through the complementary relations between the two mechanisms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".