RGB Color-Discriminable Photonic Synapse for Neuromorphic Vision System
Bibliographic record
Abstract
Abstract To emulate the functionality of the human retina and achieve a neuromorphic visual system, the development of a photonic synapse capable of multispectral color discrimination is of paramount importance. However, attaining robust color discrimination across a wide intensity range, even irrespective of medium limitations in the channel layer, poses a significant challenge. Here, we propose an approach that can bestow the color-discriminating synaptic functionality upon a three-terminal transistor flash memory even with enhanced discriminating capabilities. By incorporating the strong induced dipole moment effect at the excitation, modulated by the wavelength of the incident light, into the floating gate, we achieve outstanding RGB color-discriminating synaptic functionality within a remarkable intensity range spanning from 0.05 to 40 mW cm −2 . This approach is not restricted to a specific medium in the channel layer, thereby enhancing its applicability. The effectiveness of this color-discriminating synaptic functionality is demonstrated through visual pre-processing of a photonic synapse array, involving the differentiation of RGB channels and the enhancement of image contrast with noise reduction. Consequently, a convolutional neural network can achieve an impressive inference accuracy of over 94% for Canadian-Institute-For-Advanced-Research-10 colorful image recognition task after the pre-processing. Our proposed approach offers a promising solution for achieving robust and versatile RGB color discrimination in photonic synapses, enabling significant advancements in artificial visual systems.
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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) | 0.002 | 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".