Implantable Neural Probe with Thermo-Optic Switches Based on Multimode Interference (MMI) in Thermogenetic Application
Bibliographic record
Abstract
This paper presents the design of an implantable neural probe with 8 optical switches that can route the beams in the different brain regions which are sensitive to near-infrared (1550 nm) for thermogenetic applications. The presented probe, which is fabricated in a standard SOI technology, can enable robust activation of neurons using thermosensitive transient receptor potential (TRP) cation channels with a higher spatial and temporal resolution, thanks to the utilization of optical switches and grating couplers with precise spot stimulation. Each optical switch is linked to a waveguide and a grating coupler, enabling simultaneous stimulation of different sites within the brain. The activation or deactivation of optical switches allows controlling passage of light through the desired waveguide to effectively providing optical stimulation to specific neurons or neural circuits. The optical switch works based on the thermo-optics effect that consists of a silicon Multimode Interference (MMI) as an optical splitter and a Titanium nitride (TiN) metal heater that is used for changing the refractive index for π-phase shifting. The measured π-phase shifting power was 19.8 mW with an extinction ratio of 23 dB at 1550 nm. Also, we used a TiN metal as a temperature sensor for controlling the heater. The difference in the temperature sensor impedance was 0.7 Ω when the power was applied to the heater.
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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) | 0.001 | 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".