All-optical organic photochemical integrated nanophotonic memory: low-loss, continuously tunable, non-volatile
Bibliographic record
Abstract
Controlling changes in the optical properties of photonic devices allows photonic integrated circuits (PICs) to perform useful functions, leading to a large breadth of applications in communications, computing, and sensing. Many mechanisms to change optical properties exist, but few allow doing so in a reversible, non-volatile manner. Without such mechanisms, power inefficiencies and use of external memory are inevitable. In this work, we propose and experimentally demonstrate reversible, non-volatile phase actuation of a silicon nitride PIC with thermally stable photochromic organic molecules vapor-deposited within a slot waveguide structure. The use of a high-core-index platform allows the photochemical phase actuation of a planar-resonator-based photonic memory unit, which enables positive and negative signal weighting and permits integrated spectroscopic analysis. We show properties of this all-optical memory for a silicon photonics platform, including low loss in the optical C-band, first-order photokinetics of the photoconversion, bidirectional scalable switching, and continuous tuning. Such features are critical for memories in analog applications such as quantum, microwave, and neuromorphic photonics, where bipolar weights, low loss, and precision are paramount. More generally, this work suggests that back-end-of-line-compatible vapor deposition of organic molecules into silicon photonic circuits is promising to introduce non-silicon-native functionality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".