Signal dependent interpixel capacitance in hybridized arrays: simulation, characterization, and correction
Bibliographic record
Abstract
Interpixel capacitance (IPC) is the mechanism for a type of deterministic electrostatic coupling between neighboring pixels in hybridized detector arrays. Indium bumps are used to connect the photo-diode detection layer to the read-out circuitry. These indium bumps act as wires, transmitting voltages and currents between the detector layer and the read-out layer. When placed in close proximity, the voltages of these pixels capacitively couple to each other. The result is that when the voltage on one pixel changes, the voltage on that pixel’s neighbors change as well. IPC coupling results in a blur where a fraction, called the coupling coefficient, of the signal that was generated and collected in a given pixel, appears instead as signal on the neighboring pixels. Semi-conductor and electrostatic physics simulations have been conducted which show that the IPC is expected to change depending on the relative voltages of nearby pixels. The coupling coefficient is a function of signal strength, decreasing as signal strength increases. Characterization of IPC coupling using isolated single pixel events such as hot pixels in dark frames and single pixel resets shows this same signal dependence. The signal dependence of IPC introduces a new wrinkle in using hybridized arrays for scientific imaging. Previous work has anticipated that IPC results in a blurring of signal that can result in decreased contrast, but a signal dependence can result in additional effects on astronomical data. For example, when using pointspread function (PSF) fitting techniques on crowded fields to do astrometry and photometry, the PSF distortions due to IPC can result in an underestimate for the flux of brighter sources and an overestimate for the weaker sources. For an IPC coupling coefficient on the order of 1% with a signal dependence on the order of 0.4% , the full-width-half-max (FWHM) for a source near the sensitivity limit can be as much as 1% wider than the FWHM of a source near saturation. This results in flux estimations being off on the order of 1%. PSF distortion systematically drives down measurements of separation between sources. This error in separation drops to zero for well isolated sources, but when the PSFs are confused, it can result in an underestimate of separation on the order of 1%. To correct these errors, a method to remove a signal dependent IPC using an iterative method of successive approximations has been developed.
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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 | 0.000 | 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 | 0.000 | 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 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".