Field enhanced charge carrier reconfiguration in electronic and ionic coupled dynamic polymer resistive memory
Bibliographic record
Abstract
Dynamic resistive memory devices based on a conjugated polymer composite (PPy(0)DBS(-)Li(+) (PPy: polypyrrole; DBS(-): dodecylbenzenesulfonate)), with field-driven ion migration, have been demonstrated. In this work the dynamics of these systems has been investigated and it has been concluded that increasing the applied field can dramatically increase the rate at which information can be 'written' into these devices. A conductance model using space charge limited current coupled with an electric field induced ion reconfiguration has been successfully utilized to interpret the experimentally observed transient conducting behaviors. The memory devices use the rising and falling transient current states for the storage of digital states. The magnitude of these transient currents is controlled by the magnitude and width of the write/read pulse. For the 500 nm length devices used in this work an increase in 'write' potential from 2.5 to 5.5 V decreased the time required to create a transient conductance state that can be converted into the digital signal by 50 times. This work suggests that the scaling of these devices will be favorable and that 'write' times for the conjugated polymer composite memory devices will decrease rapidly as ion driving fields increase with decreasing device size.
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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".