Photolithography-free Ge–Se based memristive arrays; materials characterization and device testing
Bibliographic record
Abstract
The focus of this work is on the formation of a lithography-free redox conductive bridge memristor array, comprised of different compositions of Ge x Se 1− x chalcogenide glasses with the aim of selecting the chalcogenide material that provides the best performance. Various memristive arrays were fabricated on a metal–chalcogenide–metal stack. This structure offers high device density with the simplest configuration and allows access to each nano redox conductive bridge device. It was found that the device stability and threshold voltage were a function of the chalcogenide glass composition, with the Ge-rich film contributing to the best device performance, which is attributed to the formation of rigid structure and the availability of Ge–Ge bonds. Additionally, these parameters were dependent on the thickness and the surface roughness of the chalcogenide glass. Application of a nonlithography method for fabricating the array structure offered excellent yield, stable ON–OFF states and good uniformity. This demonstration, along with success achieved at the single cell level, suggests that the redox conductive bridge memristor is well positioned for ultrahigh performance memory and logic applications.
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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".