High-Density Implantable Microelectrode Arrays for Brain-Machine Interface Applications
Bibliographic record
Abstract
Microelectrode arrays (MEAs) act as an interface between electronic circuits and neural tissues of implantable devices. Biological response to chronic implantation of MEAs is an essential factor in determining a successful electrode design. Finding appropriate coating materials which are biocompatible and improve electrical properties of MEAs are among the main challenges. In this paper, we propose a novel, three-dimensional (3D), high-density, silicon-based MEAs for both neural recording and stimulation. Electrodes were fabricated using micromachining techniques. Geometrical features of these electrodes not only cause less tissue damage during insertion but also provide more contacts between the electrodes and targeted neural tissues. In order to achieve the proposed geometry, we introduce a novel masking method to coat variable-height electrodes with uniform and small tip-exposure. More importantly, compared to conventional techniques, the new masking method significantly improves process time and costs. This technique needs only one step masking and reduces the conventional masking steps from ten to three. In the next step, the active sites of the electrodes were coated with thin-films of molybdenum (Mo) and platinum (Pt) due to their ability to transfer between ionic and electronic current and to resist corrosion. Electrodes were characterized by scanning electron microscopy and impedance measurements. The average impedance of Mo and Pt electrodes at 1 kHz was 350 ± 50 kΩ and 150 ± 10 kΩ, respectively.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".