Electrical Characterisation of HfZrO2 Ferroelectric Tunnel Junctions for Neuromorphic Application
Bibliographic record
Abstract
Ferroelectric Tunnel Junctions (FTJ), which can modulate their electro-resistance depending on the polarisation configuration, have demonstrated multi-state, synaptic behaviour [1]. Hf0.5Zr0.5O2 (HZO) based FTJs are an ideal solution for the industrial implementation of brain inspired computing thanks to the low annealing temperature of HZO and its full compatibility with industrial processes. In this work, we present the development of HZO-based FTJs on silicon substrates and their electrical characterisation. A TiN/HZO/Al2O3/TiN structure was fabricated by reactive magnetron sputtering with 11 nm of HZO crystallised using rapid thermal annealing [2,3]. The dielectric layer of Al2O3 was formed from the deposition of Al and the scavanging of oxygen from the HZO layer, leading to increased conductance and enhancing the asymmetry of the junction to reach higher electro-resistance values. The structural properties were investigated by X-ray reflectometry and grazing incidence X-ray diffraction. Positive-Up-Negative-Down measurements with engineered pulse parameters along with quasi-static current-voltage measurements were conducted to evaluate and control the ferroelectric switching of the devices together with their electro-resistance. Cycling measurements were carried out to investigate the evolution of the polarisation and of the resistance ratio until breakdown. Dominant conduction mechanisms across the junction were evaluated by means of in temperature current-voltage measurements and modelling. References: [1] Boyn, S., Grollier, J., Lecerf, G. et al., Nat Commun 8, 14736 (2017). [2] Bouaziz, J. et al., APL Materials 7, 081109 (2019). [3] Bouaziz, PR Romeo, N Baboux, B VilquinACS Applied Electronic Materials 1, 1740-1745 (2019)
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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 | 0.000 | 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".