Laser Driven Miniature Diamond Implant for Wireless Retinal Prostheses
Bibliographic record
Abstract
The design and benchtop operation of a wireless miniature epiretinal stimulator implant is reported. The implant is optically powered and controlled using safe illumination at near-infrared wavelengths. An application-specific integrated circuit (ASIC) hosting a digital control unit is used to control the implant's electrodes. The ASIC is powered using an advanced photovoltaic (PV) cell and programmed using a single photodiode. Diamond packaging technology is utilized to achieve high-density integration of the implant optoelectronic circuitry, as well as individual connections between a stimulator chip and 256 electrodes, within a 4.6 mm × 3.7 mm × 0.9 mm implant package. An ultrahigh efficiency PV cell with a monochromatic power conversion efficiency of 55% is used to power the implant. On-board photodetection circuity with a bandwidth of 3.7 MHz is used for forward data telemetry of stimulation parameters. In comparison to implants which utilize inductively coupled coils, laser power delivery enables a high degree of miniaturization and lower surgical complexity. The device presented combines the benefits of implant miniaturization and a flexible stimulation strategy provided by a dedicated stimulator chip. This development provides a route to fully wireless miniaturized minimally invasive implants with sophisticated functionalities.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".