Interfaces engineering to enhance ferroelectricity in ultra-thin HZO CMOS compatible FTJ
Bibliographic record
Abstract
The exponential development of artificial intelligence technologies has increased the demand of computing power and energy efficiency. In this respect, ferroelectric tunnel junction (FTJ) memristors are extremely promising components for the implementation of beyond-Von Neumann hardware architectures, e.g. brain-inspired computing, for increasing device scalability and for their ultra-low operating current [1]. The discovery of ferroelectricity in HfO2 -based materials [2] has paved the way for the integration of ferroelectric devices into the complementary-metal-oxide-semiconductor industry. Furthermore, the Hf0.5Zr0.5O2 (HZO) compound, through the Hf:Zr 1:1 substitution, has attracted great interest due to its high polarisation value and low crystallization temperature.Here, we investigate four different 6 nm-thick HZO-based heterostructures, which were entirely realized by sputtering [3], with electrodes made of titanium nitride and/or tungsten.We report a systematic and exhaustive analysis of the structural and chemical properties of the electrode/ferroelectric interfaces of different heterostructures, using a multiscale combination of techniques, such as X-ray diffraction, electron energy-loss spectroscopy, and high-resolution TEM. The results demonstrate how the top and bottom interfaces influence the ferroelectric performances of ultra-thin HZO-based tunnel devices.This work provides relevant and useful insights for the synthesis of high quality HZO-based stacks with enhanced ferroelectricity, which could serve as memory units for brain-inspired hardware architectures. Device-to-device variability is, indeed, one of the main issues to face for the realization of neuromorphic networks, where arrays of millions of memory components are used [4]. For this reason, the improvement of material engineering and optimization of device structure are critical aspects to deal with.This work is supported by the I3E ECLAUSion project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801512.References:[1] R. Berdan et al., Nat. Electron., vol. 3, no. 5, pp. 259–266, 2020.[2] T. S. Böscke et al., Appl. Phys. Lett., vol. 99, no. 10, 2011.[3] G. Segantini et al., Phys. status solidi – Rapid Res. Lett., p. 2100583, Mar. 2022.[4] A. Mehonic et al., Adv. Intell. Syst., vol. 2, no. 11, p. 2000085, Nov. 2020.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".