Asymmetric-Resistive-Switching Device with Reconfigurable Synaptic Functions for Logic-In-Memory
Bibliographic record
Abstract
Memristors are considered a very important component to build artificial neural networks and realize logic-in-memory computing that could revolutionize current von Neumann computing architectures. With a significant resistance switching behavior, memristor has ability of simulating neuromorphic computing in human brain. However, the development of memristor is restricted by reliability, manufacturing consistency, and fundamental mechanisms. To conquer these problems, a long-term stable device, the high-quality deposition method, and the investigation on the mechanism are required. In this work, a memristor with an asymmetric-resistive-switching (ARS) behavior was fabricated via the sputtering method, which is based on the structure of Mo/ZnO/In-doped Tin Oxide (ITO). It presented a unique voltage-controlled resistance switching behavior with multistate, which has long-term endurance and low volatility. It demonstrates long-term potentiation and depression characteristics. The mechanism of the unique ARS behavior was discussed. The ARS behavior could realize coupled AND and OR logic-in-memory operation. This device provides a promising application in the complex integrated circuits and artificial intelligence.
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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 | 0.000 |
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".