Charge collection efficiency in the presence of non-uniform carrier drift mobilities and lifetimes in photoconductive detectors
Bibliographic record
Abstract
We consider the charge collection efficiency (CCE) for semiconductors in which the charge transport parameters, the drift mobility μ, and the carrier lifetime τ have spatial dependence, i.e., μ = μ(x) and τ = τ(x), where x is the distance from the radiation receiving top electrode toward the rear electrode. The small signal carrier packet drift analysis (CPDA) is re-examined, and the CCE efficiency for electrons and holes is formulated in terms of μ(x)τ(x)F(x), where F is the field. We use two model mobility and lifetime variations that are linear and exponential and then calculate and compare CCE determined from the CPDA equation, numerical solution of the continuity equation and Monte Carlo simulations as a function of the parameters characterizing the linear and exponential changes. The use of standard CCE equations for nonuniform samples is extensively examined, and errors are quantified by introducing a spatial average (SA) ⟨τ(x)⟩, average inverse (AI) ⟨1/τ(x)⟩, a new effective lifetime, and a kth order average. The SA lifetime works best when τ(x) monotonically decreases with x and AI works best when τ(x) monotonically increases with x. Stabilized a-Se x-ray photoconductors were considered as a practical application of this work. Both hole and electron lifetimes decrease in a-Se upon x-ray irradiation. Using the empirical equations derived recently for τh(x) and τe(x) as a function of dose D(x) in the sample, the CCE for two a-Se samples corresponding to a low-end device quality and the “best” was determined as a function of applied field.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".