A Fully-Implantable Cochlear Implant SoC With Piezoelectric Middle-Ear Sensor and Arbitrary Waveform Neural Stimulation
Bibliographic record
Abstract
A system-on-chip for an invisible, fully-implantable cochlear implant is presented. Implantable acoustic sensing is achieved by interfacing the SoC to a piezoelectric sensor that detects the sound-induced motion of the middle ear. Measurements from human cadaveric ears demonstrate that the sensor can detect sounds between 40 and 90 dB SPL over the speech bandwidth. A highly-reconfigurable digital sound processor enables system power scalability by reconfiguring the number of channels, and provides programmable features to enable a patient-specific fit. A mixed-signal arbitrary waveform neural stimulator enables energy-optimal stimulation pulses to be delivered to the auditory nerve. The energy-optimal waveform is validated with in-vivo measurements from four human subjects which show a 15% to 35% energy saving over the conventional rectangular waveform. Prototyped in a 0.18 μm high-voltage CMOS technology, the SoC in 8-channel mode consumes 572 μW of power including stimulation. The SoC integrates implantable acoustic sensing, sound processing, and neural stimulation on one chip to minimize the implant size, and proof-of-concept is demonstrated with measurements from a human cadaver ear.
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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 | 0.000 | 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".