Surmounting Erase-Operation Limit in Organic Charge-Trap Memories by Fine Tuning Electron Injection at Semiconductor/Heterobimetallic Electrode Contacts
Bibliographic record
Abstract
Heterobimetallic systems (HBS), known for their ability to facilitate the versatile design of surface workfunctions, offer significant potential as an electron-injection electrode layer for organic semiconductors. In this paper, we propose a universal and effective strategy to overcome the limitations of the erase operation in charge-trap memory with a small-bandgap organic semiconductor or diketopyrrolopyrrole-quaterthiophene-conjugated polymer (PDPP4T) by utilizing HBS-based source/drain (SD) electrodes. Conventional gold SD electrodes restrict electron injection into the PDPP4T layer during the electrical erase operation and impose a lower limit on the erasing voltage required for full threshold-voltage recovery. The HBS SD electrodes with an upshifted Fermi level toward the lowest unoccupied molecular orbital level of PDPP4T enhanced electron injection during an electrical erase operation, reducing the erasing voltage and achieving a balance between programming and erasing voltages. In addition, an asymmetric SD structure was employed for preventing a decrease in the on-state drain current while maintaining an optimal erase operation.
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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 | 0.001 |
| 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".