Direct laser writing of copper and copper oxide structures on plastic substrates for memristor devices
Bibliographic record
Abstract
Abstract Rapid fabrication of flexible electronics is attracting much attention in many industries. There is a need to rapidly produce flexible electronic components without relying on costly precursor materials and complex processes. This work presents a direct laser writing (DLW) process capable of rapidly depositing flexible copper or copper oxide structures with a high degree of control over electrical properties. The DLW process uses a low-power fiber laser beam to selectively irradiate a thin film of copper ions to form and interconnect copper nanoparticles. The electrical properties of the deposited patterns can be controlled by tuning laser power, scanning speed, and beam defocus. The microstructures of patterns printed at varying laser powers are investigated using scanning electron microscopy, x-ray photoelectron spectroscopy, and x-ray powder diffraction and the relation between laser power and sheet resistance is explored. The results showed that high laser energy densities resulted in highly conductive patterns of metallic copper, whereas lower energy patterns resulted in copper oxide-rich patterns with significantly lower conductivity. This method can produce high-quality flexible electronic components with a range of potential applications, as demonstrated by the proof-of-concept fabrication of a flexible memristive junction with resistive switching observed at ±0.7 V and a R on/R off ratio of 102.
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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".