Realization of dual-functional resistive switching characteristics in Ag−In−Zn−S/sericin peptide-based memristive device
Bibliographic record
Abstract
Employing suitable materials and device engineering is one of the crucial methods toward the realization of multifunctional memristive devices for constructing bioinspired neuromorphic systems. In this work, dual-functional memristors composed of eco-friendly natural silk sericin, coexistently enabling the achievement of threshold switching and memory switching triggered by adjusting the compliance current value, have been fabricated with a specific two-terminal device structure: Ag/Ag−In−Zn−S/silk sericin/W. Experimentally, the as-manufactured memristors exhibit several desirable qualities, such as low switching voltage (< 0.7 V), relatively small cycle-to-cycle and device-to-device variabilities, nonvolatile multilevel storage characteristics, and rapid switching speed (40 ns). Beyond these qualities, fundamental synaptic behaviors, such as paired-pulse facilitation and spike-timing-dependent plasticity (STDP), have been mimicked. This was made possible by a filamentary mechanism based on Ag migration. The fitted time constants corresponding to the STDP potentiation and depression are about 30 ms, and the highest changes in synaptic weight for positive and negative voltage pulses are 84.4% and 61.7%, respectively. Furthermore, the typical coincidence detection task has been executed, demonstrated by simulation based on the fitted STDP's parameters of the sericin-based device. The results from this study indicate that the sericin-based memristors, as designed, have the potential to be employed in the creation of versatile neuromorphic devices for neuromorphic computing systems.
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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) | 0.000 | 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".