Near-Infrared Light Detection using CMOS Silicon Avalanche Photodiodes (SiAPDs)
Bibliographic record
Abstract
Infrared sensors have been available since the 1940s to detect, measure, and monitor the thermal radiation emitted by objects. Silicon avalanche photodiodes (SiAPDs) are a potential candidate for low-level light detection, especially in the visible and near-infrared (NIR) regions due to their bias-dependent internal gain and their ability to amplify the photogenerated signal by avalanche multiplication. SiAPDs became popular for several applications including light detection and ranging (LIDAR), military, astronomy, photon counting, and fiber optic communication. They are potential candidates for applications such as quantum cryptography, profilometry of remote objects, fluorescence spectroscopy, and biomedical imaging systems such as positron emission tomography (PET), singlephoton emission computed tomography (SPECT), and NIR spectroscopy (NIRS) as a functional and noninvasive tool for brain monitoring and imaging. In all of these applications, SiAPD plays a critical role, affecting the overall performance and functionality of the device. As an example, in NIRS, the brain tissue is illuminated by NIR radiation, and the reflected signal is observed to investigate the brain's function. In the NIR range (650- 950 nm), water has relatively low absorption, while oxy- and deoxyhemoglobin have high absorption. Due to these properties, NIR light can penetrate biological tissues in the range of 0.5-3 cm, allowing investigation of relatively deep brain tissue and a potential to differentiate between healthy and diseased tissues. A critical element for NIRS front-end receivers includes a low-noise, sensitive photodetector to ensure maximum detection of the reflected NIR light that is strongly attenuated (seven to nine orders of magnitude) by the biological tissues.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".