Enhanced Photon Detection Probability Model for Single-Photon Avalanche Diodes in TCAD with Machine Learning
Bibliographic record
Abstract
Accurate photon detection probability (PDP) modeling is important for the optimized design of single-photon avalanche diodes (SPADs) using modern standard CMOS technologies. To ensure a planar active region of a SPAD, the edge of the depletion region must have a lower electric field, so a lower doping concentration is needed. However, this edge effect may have a negative impact on the total PDP, especially for small-sized SPADs. In this paper, we proposed an enhanced PDP modeling process by combining the Technology Computer-Aided Design (TCAD) simulations with machine learning (ML) techniques. Using this ML-TCAD PDP model, we investigated the influence of the edge effect on the PDP of SPADs by varying the diameter of the SPADs from 1.75 μm to 8.75 μm. After generating the sample simulation data, Gaussian process regression (GPR) and deep neuron network (DNN) are applied to train the model. With the application of principal component analysis (PCA), the accuracy of the trained models was significantly improved. Overall, this ML-TCAD PDP model provides an optimized and accelerated design process for SPADs, thus saving simulation time and reducing the design iterations required in the traditional design process of SPADs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".