Fully CMOS-compatible passive TiO2-based memristor crossbars for in-memory computing
Bibliographic record
Abstract
Brain-inspired computing and neuromorphic hardware are promising approaches that offer great potential to overcome limitations faced by current computing paradigms based on traditional von-Neumann architecture. In this regard, interest in developing memristor crossbar arrays has increased due to their ability to natively perform in-memory computing and fundamental synaptic operations required for neural network implementation. For optimal efficiency, crossbar-based circuits need to be compatible with fabrication processes and materials of industrial CMOS technologies. Herein, we report a complete CMOS-compatible fabrication process of TiO2-based passive memristor crossbars with 700 nm wide electrodes. We show successful bottom electrode fabrication by a damascene process, resulting in an optimised topography and a surface roughness as low as 1.1 nm. DC sweeps and voltage pulse programming yield statistical results related to synaptic-like multilevel switching. Both cycle-to-cycle and device-to-device variability are investigated. Analogue programming of the conductance using sequences of 200 ns voltage pulses suggest that the fabricated memories have a multilevel capacity of at least 3 bits due to the cycle-to-cycle reproducibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".