Foundry-fabricated dual-color nanophotonic neural probes for photostimulation and electrophysiological recording
Bibliographic record
Abstract
Significance: Compact tools capable of delivering multicolor optogenetic stimulation to deep tissue targets with sufficient span, spatiotemporal resolution, and optical power remain challenging to realize. Here, we demonstrate foundry-fabricated nanophotonic neural probes for blue and red photostimulation and electrophysiological recording, which use a combination of spatial multiplexing and on-shank wavelength demultiplexing to increase the number of on-shank emitters. Aim: We demonstrate silicon (Si) photonic neural probes with 26 photonic channels and 26 recording sites, which were fabricated on 200-mm diameter wafers at a commercial Si photonics foundry. Each photonic channel consists of an on-shank demultiplexer and separate grating coupler emitters for blue and red light, for a total of 52 emitters. Approach: experiments by photostimulating through 16 of the available 26 emitter pairs. Results: We report neural probe electrode impedances, optical transmission, and beam profiles. We validated a packaged neural probe in optogenetic experiments with mice sensitive to blue or red photostimulation. Conclusions: Our foundry-fabricated nanophotonic neural probe demonstrates dense dual-color emitter integration on a single shank for targeted photostimulation. Given its two emission wavelengths, high emitter density, and long site span, this probe will facilitate experiments involving bidirectional circuit manipulations across both shallow and deep structures simultaneously.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".