A 24-Channel Neurostimulator IC With Channel-Specific Energy-Efficient Hybrid Preventive-Detective Dynamic-Precision Charge Balancing
Bibliographic record
Abstract
This paper presents the design, development, and experimental characterization of a 24-channel programmable charge-balanced current-mode neurostimulator IC. Each channel is equipped with a quad-threshold voltage-based charge imbalance detection and a dedicated hybrid preventive-detective charge balancing circuit. The interplay of the preventive and detective control loops utilized for charge balancing has resulted in minimizing the power and timing overhead of the proposed strategy for maintaining a charge-neutral electrode-tissue interface, while avoiding the risk of unintended stimulation. The design offers dynamic programmability for the safe and unsafe charge imbalance thresholds, as well as for the balancing speed and precision. The IC is fabricated in a standard 0.18 μm CMOS technology with an overall active area of 2.27mm2. Experimental characterization results of different circuit blocks are presented and discussed. Additionally, the IC's efficacy in conducting charge-balanced stimulation is experimentally validated under various scenarios and for the full range of stimulation current magnitude, showing the balancing accuracy, latency, and active time. Experiments are conducted both with a simplified electrical model of the interface impedance as well as in vitro. Compared to the state-of-the-art stimulators with a closed-loop charge balancer, the presented work offers the most energy-efficient charge balancing technique, the shortest required inter-pulse interval (i.e., neutralization time), and the highest balancing precision.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".