Multistate resistive switching behaviors for neuromorphic computing in memristor
Bibliographic record
Abstract
Conventional Von Neumann computing systems encounter increasing challenges in the big-data era due to the constraints by the separated data storage and processing. Resistive random-access memory provides dual functionalities of data storage and computing at the same position without data transmission. This is one of the most promising candidates for energy efficient neuromorphic computing. The key points to realize neuromorphic computing are the selection of functional materials, the design of multistate devices, and a complete logic function implementing in-memory computing. Here, we demonstrate a memristor device, formed by Al/TiO2–few-layer Graphene–DNA/Pt layers, with stable intermediate multistate resistive switching behaviors. Asynchronous conduction by either oxygen vacancies migration or injected electron transfer is responsible for the multistate resistive switching behaviors. For neuromorphic computing, a pixel data stored and 2-bit parallel logic computations are simulated based on the multistate resistive switching behaviors. Compared with traditional memristor devices, this device can achieve theoretically double the data storage. This work provides a new horizon on the memristive memory and the complete logic hardware.
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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".