Fabrication and development of ferroelectric memories based on Hf₀.₅Zr₀.₅O₂ using a damascene process
Bibliographic record
Abstract
The manufacturing techniques no longer allow increasing the density of electrical components without impacting power consumption. This poses major challenges for the semiconductor industry, particularly with the rise of artifcial intelligence. The traditional von Neumann architecture faces the memory bottleneck, limiting computing speed by the constant data transfer between memory and processor. To overcome this limitation, complementary architectures are needed, including neuromorphic computing, inspired by the biological brain, where memory and computation are not physically separated. Ferroelectric components ofer promising prospects for this application. Among these materials, Hf0.5Zr0.5O2 (HZO) emerges as a prime candidate due to its compatibility with CMOS technologies. This thesis aims to establish a foundation for realizing a passive HZO-based crossbar network through the damascene process. Chapter 2 presents the state-of-theart of HZO-based ferroelectric memories and discusses associated technological challenges. Chapter 3 inventories the fabrication and characterization methods used during this research project. Chapter 4 is dedicated to the development of the HZO deposition recipe and outlines how we successfully optimized the ferroelectric properties of the thin HZO layer. An analysis of electrical and physical characterizations highlights performance differences between devices fabricated with TiN and W bottom electrodes. Chapters 5 and 6 consist of two articles. The frst explores the impact of the damascene fabrication process on the performance of ferroelectric capacitors with a "crosspoint" structure. The efect of reducing the active surface area is also studied. The second focuses on the impact of reducing the thickness of HZO on the performance of crosspoint-type capacitors. These works pave the way for a better understanding of the mechanisms infuencing the performance of ferroelectric components, essential for the development of future neuromorphic crossbar networks based on ferroelectric tunnel junctions (FTJs).
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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) | 0.001 | 0.001 |
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".