CZT sensors for Computed Tomography: from crystal growth to image quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent advances in Traveling Heater Method (THM) growth and device fabrication that require additional processing steps have enabled to dramatically improve hole transport properties and reduce polarization effects in Cadmium Zinc Telluride (CZT) material. As a result high flux operation of CZT sensors at rates in excess of 200 Mcps/mm2 is now possible and has enabled multiple medical imaging companies to start building prototype Computed Tomography (CT) scanners. CZT sensors are also finding new commercial applications in non-destructive testing (NDT) and baggage scanning. In order to prepare for high volume commercial production we are moving from individual tile processing to whole wafer processing using silicon methodologies, such as waxless processing, cassette based/touchless wafer handling. We have been developing parametric level screening at the wafer stage to ensure high wafer quality before detector fabrication in order to maximize production yields. These process improvements enable us, and other CZT manufacturers who pursue similar developments, to provide high volume production for photon counting applications in an economically feasible manner. CZT sensors are capable of delivering both high count rates and high-resolution spectroscopic performance, although it is challenging to achieve both of these attributes simultaneously. The paper discusses material challenges, detector design trade-offs and ASIC architectures required to build cost-effective CZT based detection systems. Photon counting ASICs are essential part of the integrated module platforms as charge-sensitive electronics needs to deal with charge-sharing and pile-up effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it