Fabrication and characterization of a novel x-ray silicon detector
Bibliographic record
Abstract
Protein crystallography is a key method for protein structure investigation in modern medicine and X-ray diffraction detectors are key to performance. We introduced a silicon detector, based on an active-pixel readout of hydrogenated amorphous silicon (a-Si:H) thin film transistors (TFTs) for protein crystallography. In this work, we present the fabrication process of the detector array, performance of the first fabricated TFT arrays, and the performance of the TFTs in terms of fieldeffect mobility, gate material quality, and stability under long stress using a Fe-55 (50 μCi)gamma ray source (6 to 10 keV photon energies). Device fabrication was performed in an in-house facility, Giga-to-Nano microfabrication facility, at the University of Waterloo, and involved plasma enhanced chemical vapor deposition (PECVD) and wet and dry etch techniques with a simple two mask process. The TFT test results promise higher effective field effect mobility of 16.49 cm2/V·s due to the presence of silicon substrate contacting the a-Si:H channel layer along with a compromise in leakage current, yielding a 104 ON/OFF ratio. Meanwhile, the threshold voltage shift is manageable by applying a negative voltage of a duration less than 1/10 of the duty cycle. From the detector leakage test, the leakage current through the TFT gate was acceptable range while the photo-generated current needs to be suppressed with positive voltage bias at the gate electrode. Thus, minimizing the negative gate bias in readout operation is crucial. Finally, TFT readout current under the same Fe-55 X-ray source shows that optimal operation range can be determined when bulk bias is higher than TFT operation bias.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".