Photodetector Innovations for Advancing Wearable Optical Biosensors: A Review
Bibliographic record
Abstract
Optical biosensors such as photoplethysmography (PPG), pulse oximetry, and near-infrared spectroscopy (NIRS) are critical health monitoring tools, providing critical insights into physiological indicators such as heart rate (HR), oxygen saturation, blood pressure (BP), and respiratory rate (RR). At the core of these sensors are photodetectors (PDs), which are available in various types, including inorganic [such as p-n, p-i-n, avalanche, CMOS, single-photon avalanche, silicon photomultiplier (SiPM), and dynamic PDs (DPDs)] and organic (such as bulk heterojunction (BHJ), photomultiplication (PM), and hybrid CMOS PDs). The choice of a PD significantly influences optical biosensor performance by affecting key factors such as photosensitivity, detectivity, signal-to-noise ratio (SNR), dynamic range (DR), low-light detection, parasitic capacitance, dark current, noise, power consumption, and design complexity. This article provides a detailed review of the latest innovations in PDs and their associated readout circuit designs, highlighting how they enhance the performance of optical biosignal sensing. It also explores future trends in PD development, including high-mobility semiconductors, ultrathin PD layers, and novel fabrication techniques that are driving the evolution of the next-generation biosignal sensing solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".