Photon quantum shot noise limited array in amorphous silicon technology for protein crystallography applications
Bibliographic record
Abstract
An array of ring voltage controlled oscillators (RVCO) aiming for photon quantum shot noise limited applications such as protein crystallography is presented. The pixilated array consists of 24 by 21 RVCO pixels. RVCO pixel converts x-ray generated input charge into an output oscillating frequency signal. This architecture can be used in both direct and indirect detection schemes. In this paper the direct detection using a layer of amorphous selenium (a-Se) coupled with the RVCO array is proposed. Theoretical and Experimental results for an in-house fabricated array of RVCOs in amorphous silicon (a-Si) technology are presented. All different requirements for protein crystallography application are listed in this paper and also the way this array addresses each of these requirements is discussed in details in this paper. The off-panel readout circuitry, designed and implemented in-house, is given in this paper. The off-panel readout circuits play an important role in the imaging applications using frequency based pixels. They have to be optimized in order to reduce the fixed pattern noise and fringing effects in an imaging array containing many such RVCO pixels. Since the frequency of oscillation of each of these pixels is in the range of 100 KHz, there is no antenna effect in the array. Antenna effect becomes an important issue in other technologies such as poly silicon (poly-Si) and CMOS technologies due to higher frequency of oscillation ranges (more than 100 MHz). Noise estimations, stability simulations and measurements for some randomly selected pixels in the array for the fabricated RVCO array are presented. The reported architecture is particularly promising for large area photon quantum shot noise applications, specifically protein crystallography. However, this architecture can be used for low dose fluoroscopy, dental computed tomography (CT) and other large area imaging applications limited by input referred electronic noise due to its very low input referred electronic noise, high sensitivity and ease of fabrication in low cost a-Si technology.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".