A Complementary Metal Oxide Semiconductor Process-Compatible Ferroelectric Tunnel Junction
Bibliographic record
Abstract
In recent years, experimental demonstration of ferroelectric tunnel junctions (FTJ) based on perovskite tunnel barriers has been reported. However, integrating these perovskite materials into conventional silicon memory technology remains challenging due to their lack of compatibility with the complementary metal oxide semiconductor process (CMOS). This communication reports the fabrication of an FTJ based on a CMOS-compatible tunnel barrier Hf 0.5 Zr 0.5 O 2 (6 unit cells thick) on an equally CMOS-compatible TiN electrode. Analysis of the FTJ by grazing angle incidence X-ray diffraction confirmed the formation of the noncentrosymmetric orthorhombic phase ( Pbc 2 1, ferroelectric phase). The FTJ characterization is followed by the reconstruction of the electrostatic potential profile in the as-grown TiN/Hf 0.5 Zr 0.5 O 2 /Pt heterostructure. A direct tunneling current model across a trapezoidal barrier was used to correlate the electronic and electrical properties of our FTJ devices. The good agreement between the experimental and theoretical model attests to the tunneling electroresistance effect (TER) in our FTJ device. A TER ratio of ∼15 was calculated for the present FTJ device at low read voltage (+0.2 V). This study suggests that Hf 0.5 Zr 0.5 O 2 is a promising candidate for integration into conventional Si memory technology.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".