Toward an Energy-Efficient High-Voltage Compliant Visual Intracortical Multichannel Stimulator
Bibliographic record
Abstract
We present, in this paper, a new multichip system aimed toward building an implantable visual intracortical stimulation device. The objective is to deliver energy-optimum pulse patterns to neural sites with needed compliance voltage across high electrode–tissue interface impedance of implantable microelectrodes. The first chip is an energy-efficient stimuli generator (SG), and the second one is a high-impedance microelectrode array driver (MED) output stage. The four-channel SG produces rectangular, half-sine, plateau-sine, and other types of current pulse with stimulation current ranging from 2.32 to 220$\mu \text{A}$per channel. The microelectrode array driver is able to deliver 20 V per anodic or cathodic phase across the microelectrode–tissue interface for ±13 V power supplies. The MED supplies different current levels with the maximum value of 400$\mu \text{A}$per input and 100$\mu \text{A}$per output channel simultaneously to 8–16 stimulation sites through microelectrodes, connected either in bipolar or monopolar configuration. Both chips receive power via inductive link and data through capacitive coupling. The SG and MED chips have been fabricated in 0.13-$\mu \text{m}$CMOS and 0.8-$\mu \text{m}$5-/20-V CMOS/double-diffused metal-oxide-semiconductor technologies. The measured dc power budgets consumed by low- and mid-voltage chips are 2.56 and 2.1 mW consecutively. The system, modular in architecture, is interfaced with a newly developed platinum-coated pyramidal microelectrode array.In vitrotest results with 0.9% phosphate buffer saline show the microelectrode impedance of 70$\text{k}\Omega $at 1 kHz.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".